Patent Publication Number: US-2023157762-A1

Title: Extended Intelligence Ecosystem for Soft Tissue Luminal Applications

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Patent Application Ser. No. 63/282,257 (the “&#39;257 application”), filed Nov. 23, 2021, by Peter N. Braido et al. (attorney docket no. A0007989US01), entitled, “Extended Intelligence Ecosystem for Soft Tissue Luminal Applications,” the disclosure of which is incorporated herein by reference in its entirety for all purposes. 
     This application may also be related to each of U.S. Patent Application Ser. No. 63/032,278 (the “&#39;278 application”), filed May 29, 2020, by Peter N. Braido et al. (attorney docket no. A0003763US01), entitled, “Intelligent Assistance (IA) Ecosystem,” U.S. Patent Application Ser. No. 63/032,283 (the “&#39;283 application”), filed May 29, 2020, by Peter N. Braido et al. (attorney docket no. C00017370US01), entitled, “Extended Reality (XR) Applications for Cardiac Arrhythmia Procedures,” U.S. Patent Application Ser. No. 63/032,289 (the “&#39;289 application”), filed May 29, 2020, by Peter N. Braido et al. (attorney docket no. C00017918US01), entitled, “Extended Reality (XR) Applications for Cardiac Blood Flow Procedures,” and U.S. Patent Application Ser. No. 63/058,632 (the “&#39;632 application”), filed Jul. 30, 2020, by Peter Braido et al. (attorney docket no. A0004098US01), entitled, “Extended Reality (XR) Applications for Cardiac Shunting Procedures,” the disclosure of each of which is incorporated herein by reference in its entirety for all purposes. This application may also be related to U.S. patent application Ser. No. 17/334,487 (the “&#39;487 application”), filed May 28, 2021, by Peter N. Braido et al. (attorney docket no. A0003763US02), entitled, “Intelligent Assistance (IA) Ecosystem,” which claims priority to the &#39;278, &#39;283, &#39;289, and &#39;632 Applications, the disclosure of each of which is incorporated herein by reference in its entirety for all purposes. This application may also be related to U.S. Patent Application Ser. No. 63/187,738 (the “&#39;738 Application”), filed May 12, 2021, by Peter N. Braido et al. (attorney docket no. A0005475US01), entitled, “Extended Intelligence for Cardiac Implantable Electronic Device (CIED) Placement Procedures,” the disclosure of which is incorporated herein by reference in its entirety for all purposes. 
     The respective disclosures of these applications/patents (which this document refers to collectively as the “Related Applications”) are incorporated herein by reference in their entirety for all purposes. 
    
    
     FIELD 
     The present disclosure relates, in general, to methods, systems, and apparatuses for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications (including, but not limited to, cardiovascular, pulmonary, gastrointestinal, neurovascular, or peripheral vascular applications, or the like). 
     BACKGROUND 
     Many patients go untreated for severe cardiac and other conditions that can be endoluminally accessed due to a lack of an adequate ecosystem of medical tools. One such condition is Bradycardia, which is a disruption in the heart&#39;s electrical system that causes a slow heart rate, that if left untreated can result in cardiac arrest. For patients with atrioventricular (“AV”) block, dual chamber synchronization (as opposed to the current practice of single chamber pacing) may be significantly more effective. Specifically, 80% of Bradycardia have dual chamber needs but only 20% of the patient population are treated via a single chamber. 
     Conventional treatments for Bradycardia have the following high level problems: (i) complex, expensive procedures with novel workflows face adoption barriers; (ii) inadequate, insufficient, and/or minimal enablers for remote management; (iii) inadequate, insufficient, and/or minimal way to facilitate or improve differential diagnosis with; (iv) lack of information to guide proper treatment options; (v) lack of guidance as to 30 day readmission rates being a problem or not and as to what strategies currently exist to prevent them; and/or (vi) lack of guidance as to whether there are any wearable or external devices that are used or investigated for the remote management and prevention; and/or the like. 
     More recently, the use of augmented reality or mixed reality to aid in the medical professional during operations or procedures has led to improvements in the system, allowing for more successful outcomes to the operations or procedures. Such recent developments, however, do not fully implement compilation of surgical tool or instrument data, imaging data, and patient data, or integrate the compilation of such data with data analytics and artificial intelligence (“AI”) or machine learning or deep learning, and with an intuitive extended reality (“XR”) implementation, and, in some cases, also with interfacing robotics to achieve an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem as described in detail below. 
     Hence, there is a need for more robust and scalable solutions for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing IA or EI ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications. 
     SUMMARY 
     The techniques of this disclosure generally relate to tools and techniques for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing IA or EI ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications. 
     In an aspect, a method may comprise receiving, using a computing system, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room; and receiving, using the computing system, one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices. The method may also comprise analyzing, using the computing system, the received one or more first layer input data and the received one or more second layer input; and generating, using the computing system, one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient, wherein the one or more soft tissue luminal portions comprise at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient. The method may further comprise generating, using the computing system, one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations; and presenting, using the computing system and using a user experience (“UX”) device, the generated one or more XR images. 
     In some embodiments, the computing system may comprise at least one of an XR computing system, a medical procedure computing system, a hub computing system, a 3D graphical processing unit, a cluster computing system, a 4D graphics computing system, a server computer, a cloud computing system, or a distributed computing system, and/or the like. 
     According to some embodiments, the one or more surgical devices may comprise at least one of one or more catheters, one or more catheter interconnect cables, one or more valves, one or more balloons, one or more leads, one or more rigid robotic devices, one or more soft robotic devices, one or more robotic systems, one or more robotic arms, one or more handheld robotic systems, one or more robotic systems integrated into a device handle, one or more stents, one or more needles, one or more grafts, one or more occluders, one or more shunts, one or more therapeutic delivery devices, one or more implant delivery devices, one or more diagnostic devices, one or more diagnostic catheters, one or more bronchoscopes, one or more implant devices, one or more surgical tools, one or more delivery pharmaceuticals, one or more biopsy tools, one or more excision tools, one or more ablation tools, one or more monitoring devices, one or more cameras, one or more imaging tools, one or more fiducials, one or more staples, one or more anchors, one or more meshes, one or more vascular cannulae, one or more circulatory pumps, one or more valve repair devices, one or more embolic protection devices, one or more cardiomyoplasty tools, a pulmonary artery pressure sensing device, one or more vascular closure tools, one or more septal closure tools, one or more ventricular closure tools, one or more lasers, one or more plaque removal tools, one or more guide wires, one or more introducers, one or more sheaths, one or more PillCams, one or more clips, one or more capsules, one or more energy delivery tools, a pulmonary vein ablation catheter (“PVAC”), a pulsed field ablation (“PFA”) system, a PFA console, an electroporation system, an electroporation control console, a cryoballoon or a cryoablation catheter, a cryoablation console, a radio frequency (“RF”) ablation-based system, an RF ablation control console, a phased RF (“pRF”) ablation-based system, an pRF ablation control console, a laser ablation-based system, a laser ablation control console, a radiation ablation-based system, a radiation ablation control console, a microwave ablation-based system, a high intensity focused ultrasound (“HIFU”) system, a HIFU control console, an implantable cardioverter defibrillator (“ICD”) device, an extravascular ICD (“EV-ICD”), a miniature leadless implant, a miniature leadless pacemaker delivery system, a miniature leadless pacemaker, or one or more capital equipment, and/or the like. 
     In some embodiments, the one or more patient sensor data may be obtained using one or more sensors comprising at least one of one or more chronically implanted sensors, one or more diagnostic sensors, one or more surgical sensors, one or more wearable sensors, one or more gas sensors, one or more optical sensors, one or more impedance sensors, one or more ultrasound sensors, one or more flow sensors, one or more blood velocity sensors, one or more blood volume sensors, one or more electrical sensors, one or more voltage sensors, one or more amperage sensors, one or more wattage sensors, one or more motion sensors, one or more sound sensors, one or more blood pressure sensors, one or more heart rate sensors, one or more pulse sensors, one or more oxygen sensors, one or more carbon dioxide (“CO 2 ”) sensors, one or more fluid levels, one or more lung volume sensors, one or more tidal volume sensors, one or more lung filling pressure sensors, a pulmonary artery pressure sensor, one or more piezoelectric sensors, one or more accelerometers, one or more image sensors, one or more acoustic sensors, one or more temperature sensors, one or more ambulatory monitoring sensors, one or more patient weight sensors, one or more patient mattress sensors, one or more doppler sensors, one or more biomarker sensors, one or more perfusion sensors, one or more electromyography (“EMG”) sensors, one or more electrocardiography (“ECG”) sensors, one or more electromechanical wave imaging (“EWI”) system sensors, one or more electroanatomic mapping (“EAM”) system sensors, one or more sleep sensors, one or more cardiac hemodynamics sensors, one or more ischemia sensors, one or more hematocrit (“HCT”) level sensors, one or more biometric sensors, one or more electroencephalographic (“EEG”) sensors, one or more apnea monitoring sensors, one or more dyspnea monitoring sensors, one or more nociception monitoring sensors, or one or more pain sensors, and/or the like. 
     According to some embodiments, the one or more patient imaging data may be obtained using one or more imaging devices comprising at least one of a magnetic resonance imaging (“MRI”) system, a diffusion-tensor imaging (“DTI”) system, a computed tomography (“CT”) system, an intraoperative 2D/3D imaging system (“O-Arm”), an ultrasound (“US”) system, a transesophageal echocardiography (“TEE”) system, an intra-cardiac echocardiography (“ICE”) system, a transthoracic echocardiography (“TTE”) system, an intravascular ultrasound (“IVUS”) system, an endobronchial ultrasound system (“EBUS”), an endoscopic ultrasound system (“EUS”), an electromechanical wave imaging (“EWI”) system, a neuro-endoscopy system, a single photon emission computed tomography (“SPECT”) system, a magnetic resonance angiography (“MRA”) system, a computed tomography angiography (“CTA”) system, a blood oxygen-level dependent signal (“BOLD”) system, an arterial spin labeling (“ASL”) system, a magnetoencephalography (“MEG”) system, a positron emission tomography (“PET”) system, an electroencephalography (“EEG”) system, an optical coherence tomography (“OCT”) system, an optical imaging spectroscopy (“OIS”) system, a magnetic resonance spectroscopy (“MRS”) system, a dynamic susceptibility contrast (“DSC”) MRI system, a fluid-attenuated inversion recovery (“FLAIR”) system, a fluoroscopy system, a biplane fluoroscopic or cineradiographic system, a rotational angiographic system, an X-ray system, a 3D scanning system, an infrared (“IR”) system, an ultraviolet (“UV”) system, a bioluminescent system, an endoscopy system, a triboluminescence system, an image fusion system, a borescope, a video camera, a PillCam, or a microscope, and/or the like. 
     In some instances, the soft tissue luminal procedure may comprise at least one of an atrioventricular dual chamber sensing and pacing procedure, a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), an endoluminal procedure, a cardiac endoluminal procedure, a pulmonary endoluminal procedure, a gastrointestinal endoluminal procedure, a neurovascular endoluminal procedure, a peripheral vascular endoluminal procedure, a surgical procedure, a left atrial appendage (“LAA”) procedure, a tissue ablation procedure, a transcatheter aortic valve repair (“TAVr”) procedure, a transcatheter aortic valve replacement (“TAVR”) procedure, a transcatheter mitral valve repair (“TMVr”) procedure, a transcatheter mitral valve replacement (“TMVR”) procedure, a transcatheter pulmonic valve repair (“TPVr”) procedure, a transcatheter pulmonic valve replacement (“TPVR”) procedure, a transcatheter tricuspid valve repair (“TTVr”) procedure, a transcatheter tricuspid valve replacement (“TTVR”) procedure, a mitral clip repair procedure, a shunt procedure, a coronary angioplasty procedure, a balloon angioplasty, a stenting procedure, an atrial septal defect (“ASD”) treatment procedure, a cardiac shunt treatment procedure, a heart bypass procedure, a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a catheter ablation procedure, an endovascular repair procedure, a heart monitor installation procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a minimally invasive endovascular repair procedure, a miniature leadless implant installation procedure, a miniature leadless pacemaker installation procedure, an implantable sensor installation procedure, a surgical heart valve repair and replacement procedure, a transcatheter pulmonary valve (“TPV”) therapy, a ventricular assist device (“VAD”) installation procedure, an intra-aortic balloon pump (“IABP”) implantation procedure, a heart transplant operation, a cryoballoon or cryoablation catheter procedure, a pulsed field ablation (“PFA”) procedure, an electroporation procedure, a radio frequency (“RF”) ablation procedure, a phased RF (“pRF”) ablation procedure, a microwave (“MW”) ablation procedure, a laser ablation procedure, a radiation ablation procedure, a microwave ablation procedure, a high intensity focused ultrasound (“HIFU”) procedure, a histotripsy procedure, an abdominal aortic aneurysm (“AAA”) procedure, a thoracic aortic aneurysm (“TAA”) procedure, a thoracoabdominal aortic aneurysm (“TAAA”) procedure, a complex aortic arch aneurysm procedure, a vascular occlusion procedure, an atherectomy procedure, a renal denervation procedure, a deep vein thrombosis (“DVT”) procedure, a thrombectomy procedure, a flow diversion endoluminal procedure, or a neuro stenting procedure, and/or the like. 
     In some cases, the one or more XR images may comprise at least one of one or more augmented reality (“AR”) images, one or more AR videos, one or more virtual reality (“VR”) images, one or more VR videos, one or more mixed reality (“MR”) images, one or more MR videos, one or more XR images, or one or more XR experiences, and/or the like. 
     In some embodiments, the UX device may comprise at least one of a headset, UX glasses, a viewing window, a supplement to existing glasses, headphones, UX contact lenses, a heads-up display (“HUD”) device, a 3D spatial sound system, a telemonitoring system, a rigid robotic device control and sensory feedback system, a soft robotic device control and sensory feedback system, an eye control system, a voice control system, a remote control system, a gesture-based control system, a sign language-based control system, a body-part-based control system, a joystick, a mouse, a two-dimensional (“2D”) screen display, a 3D refractive display, a parallel reality system, a projection system, a 3D printed reconstruction system, a customized view generation system, a ghosting and prediction system, a master-slave control system, an annotation system, or a haptic feedback system, and/or the like. 
     In some instances, the generated one or more XR images may be presented to provide one or more of: a guide for the medical professional, a navigation tool during the soft tissue luminal procedure, a proximity detection tool during the soft tissue luminal procedure, a 3D or 4D visualization view of the at least one or more portions of the patient, a 3D or 4D visualization view of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of at least one of the one or more first layer input data, a heads-up display of at least one of the one or more patient sensor data, a heads-up display of at least one of the one or more patient imaging data, a heads-up display of physiological data of the patient, or a heads-up display of procedure-related data of the patient, and/or the like. 
     According to some embodiments, the method may further comprise tracking, using the computing system, the one or more surgical devices, using at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system, and/or the like. 
     In some embodiments, the method may further comprise receiving, using the computing system, one or more control inputs from the medical professional; analyzing, using the computing system, the received one or more control inputs in conjunction with analysis of the received one or more first layer input data and the received one or more second layer input data; generating, using the computing system, one or more control instructions based at least in part on the analysis, the generated one or more control instructions taking into account movement including at least one of movement of one or more soft tissue luminal portions and surrounding tissue due to at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, and/or the like; and sending, using the computing system, the generated one or more control instructions to the robotic system to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient. 
     In some cases, at least the processes of receiving the one or more first layer input data, receiving the one or more second layer input data, analyzing the received one or more first layer input data and the received one or more second layer input, generating the one or more recommendations, generating the one or more XR images, presenting the generated one or more XR images, receiving the one or more control inputs, analyzing the received one or more control inputs, generating the one or more control instructions, and sending the generated one or more control instructions may occur in a manner that is at least one of continual, dynamic, feedback-looped, updated, in real-time, or in near-real-time, and/or the like, during the course of the soft tissue luminal procedure. 
     Alternatively, or additionally, the received one or more control inputs may comprise hand-movement-based control inputs resulting from movement of one or more hands of the medical professional, wherein analyzing the received one or more control inputs may comprise determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional, and wherein generating the one or more control instructions may comprise, based on a determination that the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the medical professional, generating, using the computing system, one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more particular control inputs that are based on excessive movement of the at least one hand of the medical professional. 
     According to some embodiments, the method may be performed without use of fluoroscopy. 
     In some embodiments, the soft tissue luminal procedure comprises a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), wherein the one or more surgical devices comprise a miniature leadless device (e.g., implant or pacemaker, etc.), and the method may further comprise: tracking, using the computing system, the miniature leadless device as the miniature leadless device is navigated within the body of the patient, via one of a jugular access or a femoral access, toward the heart of the patient; presenting, using the computing system and using the UX device, the generated one or more XR images to guide, in real-time or near-real-time, the medical professional in positioning the miniature leadless device within one or more predetermined or real-time adjusted targeted locations within the heart, which is in motion due to expected cardiac activity; and presenting, using the computing system and using the UX device, the generated one or more XR images to highlight, in real-time or near-real-time, at least one of the one or more targeted locations, one or more guided paths or trajectories toward each of the one or more targeted locations, or one or more portions of the heart or other organ structures to avoid, and/or the like. According to some embodiments, the method may further comprise sending, using the computing system, one or more sets of instructions generated by a programmer system, the one or more sets of instructions being configured to program one or more settings or configurations of the miniature leadless device, wherein the one or more settings or configurations of the miniature leadless device comprise at least one of pacing mode, rate limits, stimulation parameters, sensing parameters, rate response parameters, or other parameters related to operation of the miniature leadless device, or the like. In some instances, navigating the miniature leadless device within the body of the patient may be performed using one or more robotic systems controlled by one or more control inputs received from the medical professional via the computing system. 
     In another aspect, an apparatus might comprise at least one processor and a non-transitory computer readable medium communicatively coupled to the at least one processor. The non-transitory computer readable medium might have stored thereon computer software comprising a set of instructions that, when executed by the at least one processor, causes the apparatus to: receive one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room; receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices; analyze the received one or more first layer input data and the received one or more second layer input; generate one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient, wherein the one or more soft tissue luminal portions comprise at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient; generate one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations; and present, using a user experience (“UX”) device, the generated one or more XR images. 
     In yet another aspect, a system might comprise a computing system, which might comprise at least one first processor and a first non-transitory computer readable medium communicatively coupled to the at least one first processor. The first non-transitory computer readable medium might have stored thereon computer software comprising a first set of instructions that, when executed by the at least one first processor, causes the computing system to: receive one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room; receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices; analyze the received one or more first layer input data and the received one or more second layer input; generate one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient, wherein the one or more soft tissue luminal portions comprise at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient; generate one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations; and present, using a user experience (“UX”) device, the generated one or more XR images. 
     Various modifications and additions can be made to the embodiments discussed without departing from the scope of the invention. For example, while the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combination of features and embodiments that do not include all of the above-described features. 
     The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A further understanding of the nature and advantages of particular embodiments may be realized by reference to the remaining portions of the specification and the drawings, in which like reference numerals are used to refer to similar components. In some instances, a sub-label is associated with a reference numeral to denote one of multiple similar components. When reference is made to a reference numeral without specification to an existing sub-label, it is intended to refer to all such multiple similar components. 
         FIG.  1    is a schematic diagram illustrating a system for implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
         FIG.  2    is a schematic diagram illustrating a non-limiting example of building blocks for an IA or EI ecosystem that may be implemented for soft tissue luminal applications, in accordance with various embodiments. 
         FIG.  3    is a schematic diagram illustrating a non-limiting example of a process stack for implementing an IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
         FIG.  4    is a flow diagram illustrating a method for implementing an IA ecosystem, in accordance with various embodiments. 
         FIG.  5 A  is a flow diagram illustrating a non-limiting example of feedbacked interactions among three sub-layers of imbedded and contactless sensors, data, and vision systems that may be used as part of an IA or EI ecosystem implementation for soft tissue luminal applications, in accordance with various embodiments. 
         FIG.  5 B  is a flow diagram illustrating a non-limiting example of interactions among “pre-operative planning,” “intra-operative adjustments,” and “post-operative monitoring” with an optimization feedback loop, in accordance with various embodiments. 
         FIGS.  6 A- 6 N  are diagrams illustrating non-limiting examples of one or more surgical devices and techniques for implementing a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”) for controlling positioning of the surgical device within the heart that may be part of implementation of an IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
         FIGS.  7 A- 7 D  are flow diagrams illustrating a method for implementing an IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
         FIG.  8    is a block diagram illustrating an exemplary computer or system hardware architecture, in accordance with various embodiments. 
         FIG.  9    is a block diagram illustrating a networked system of computers, computing systems, or system hardware architecture, which can be used in accordance with various embodiments. 
     
    
    
     DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS 
     Overview 
     In various embodiments, a computing system might receive one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room. The computing system might receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices, and/or the like. 
     The computing system might analyze the received one or more first layer input data and the received one or more second layer input, and might generate one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient. In some embodiments, the one or more soft tissue luminal portions comprise at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient, and/or the like. The computing system might then generate one or more XR images (or one or more XR experiences), based at least in part on the generated one or more recommendations, and might present the generated one or more XR images (or one or more XR experiences) using a UX device. 
     According to some embodiments, the generated one or more XR images might be presented to provide one or more of: a guide for a medical professional, a navigation tool during the soft tissue luminal procedure, a proximity detection tool during the soft tissue luminal procedure, a 3D or 4D visualization view of the at least one or more portions of the patient, a 3D or 4D visualization view of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of at least one of the one or more first layer input data, a heads-up display of at least one of the one or more patient sensor data, a heads-up display of at least one of the one or more patient imaging data, a heads-up display of physiological data of the patient, or a heads-up display of procedure-related data of the patient, and/or the like. In some instances, generating the one or more XR images might comprise combining or mapping the received one or more first layer input data and the received one or more second layer input into a combined 3D or 4D representation, based at least in part on the analysis and the generated one or more recommendations; and generating the one or more XR images based on the combined 3D or 4D representation. 
     According to some embodiments, the computing system might receive one or more control inputs from the medical professional; might analyze the received one or more control inputs in conjunction with analysis of the received one or more first layer input data and the received one or more second layer input data; might generate one or more control instructions based at least in part on the analysis, the generated one or more control instructions taking into account movement including at least one of movement of one or more soft tissue luminal portions and surrounding tissue due to at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, and/or the like; and might send the generated one or more control instructions to the robotic system to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient. 
     In some instances, at least the processes of receiving the one or more first layer input data, receiving the one or more second layer input data, analyzing the received one or more first layer input data and the received one or more second layer input, generating the one or more recommendations, generating the one or more XR images, presenting the generated one or more XR images, receiving the one or more control inputs, analyzing the received one or more control inputs, generating the one or more control instructions, and/or sending the generated one or more control instructions may occur in a manner that is at least one of continual, dynamic, feedback-looped, updated, in real-time, or in near-real-time, and/or the like, during the course of the soft tissue luminal procedure. 
     In some embodiments, the received one or more control inputs may comprise hand-movement-based control inputs resulting from movement of one or more hands of the medical professional. In such cases, analyzing the received one or more control inputs may comprise determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional. As such, generating the one or more control instructions may comprise, based on a determination that the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the medical professional, the computing system generating one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more particular control inputs that are based on excessive movement of the at least one hand of the medical professional. 
     In various aspects, decision support systems (i.e., Extended Intelligence or EI systems) are supporting clinicians at an ever-increasing rate, which has been bolstered by advances in machine learning, contactless sensors, robotics, and extended reality (among other parts of the ecosystem as shown and described with respect to the figures). These decision support systems enable seamless integration of the complex workflows, equipment, and devices so augmented clinical decision-making affords the patient with safer, more efficacious, consistent, and timely outcomes. 
     Most physical workspaces are sensitive and responsive to the non-contact and contact interactions between humans and the medical equipment in such workspaces, so by gathering this data without interfering with the workflow via contactless sensors, analyzing via machine learning algorithms, then displaying what is needed at the right time and place via extended reality systems (at least in part) will avoid medical staff cognitive overload, inefficiencies, and sub-optimal outcomes (i.e., inaccurate or imprecise positioning of catheters for implementing soft tissue luminal applications), or the like. The IA or EI ecosystem may also compensate for at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system and a flexible bronchoscope, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, as well as excessive movement of the user or physician, while reducing or eliminating the use of fluoroscopy and contrast. 
     For VFA, modifications to a miniature leadless device (e.g., implant or pacemaker, etc.) may accomplish sensing in the atrium and pacing in the ventricle, hence a leadless ventricle from atrium sensing and pacing system procedure (also referred to as a ventricle from atrial (“VFA” or “VfA”) procedure). However, the AI or EI ecosystem may be built around this device to simplify this procedure to be reliable and repeatable is necessitated to fully its potential. The VFA procedure (and many similar cardiac or endoluminal procedures) have many limitations that are addressed by the inventions described herein, where the goal is to ultimately democratize a complex procedure for pre-op, intra-op, and post-op workflow. The AI or EI ecosystem addresses the problems of the conventional treatments for Bradycardia, by: (i) providing procedures with novel workflows that are intuitive and easy to use; (ii) providing enablers for remote management; (iii) providing a way to facilitate or improve differential diagnosis with; (iv) providing a user interface to display proper treatment options; (v) providing guidance as to 30 day readmission rates being a problem or not and as to what strategies currently exist to prevent them; and/or (vi) providing guidance as to whether there are any wearable or external devices that are used or investigated for the remote management and prevention; and/or the like. 
     These and other aspects of the extended intelligence for soft tissue luminal applications (including, but not limited to, cardiovascular, pulmonary, neurovascular, or peripheral vascular applications, or the like, each with their own unique set of problems and merged technologies, etc.) are described in greater detail with respect to the figures. With respect to these soft tissue luminal applications, the EI ecosystem provides one or more of the following features: (1) moving the display screen to XR viewing of placement and windows in an ergonomic location not looking away from the patient or preferred field of view; (2) overlaying imaging modality information (e.g., CT or other imaging modality information for anatomy, morphology, etc.; MRI for tracking tissue changes such as ablation temperature changes in tissue; EAM or ECG information to see electrical flow; ultrasound or pressure catheter information to see blood flow; etc.) on the patient or preferred field of view in 3D with one or more various navigation and/or optical views in real time; (3) placing imaging in the proper viewing location and orientation anywhere in the environment; (4) identifying adjacent structures and heart/lung/lumen movement via sensor, imaging, and/or FEA/CFD algorithms; (4a) using the impedance of the tip electrode to detect perforation into the LV as intraoperative sensors and the intraoperative analytics; (5) providing the following examples of aortic implant system features: (5a) providing differentiation of rapid progressors compared with stable disease progression; (5b) providing feedback on forces incurred during the tracking and deployment of devices to reduce vascular trauma; (5c) providing feedback from the delivery system of the presence of friable material that would pose an embolic risk; (5d) enabling detection of stent graft infection (e.g., temperature chance, pH change, antibody screening) in order to detect early and treat that does not require removal of device; (5e) provide ability to detect passivation of the implant (e.g., protein absorption and endothelialization); (5f) enable tissue sensor on EndoAnchor to detect penetration into vessel wall, versus fabric only, or a complete miss; (5g) providing pressure sensors, impedance sensors, etc., to detect and display correct angle for HeliFx; (6) adding trajectory bounds from pre-op planning manually or via feedback loop from post-op database; (7) enabling the application to target two or more targets simultaneous to apply therapy, as opposed to one location; (8) enabling application targets placed on simultaneous anatomical or locational, electrical, and blood flow data feedback, thus providing integration of multiple sensor types and inputs; (9) providing real time 3D visual feedback in the form of distance to target, audible beeps, color gradation, haptic shake of handles, etc.; (10) providing contact highlighting and ablation tagging in 3D with embedded meta data in such locations in 3D coordinates, ablation metrics like power, time to target, proximity to adjacent structures, etc.; (11) recording the above data streams for post-op assessment and playback manually, and linking to acute and chronic outcomes to build the virtuous loop of AI infrastructure (e.g., using a standalone programmer system, by creating a link between the programmer system and the AI or EI ecosystem, with all the data from the pacer and programmer system being integrated with the rest of the data for impedance checks, ECG morphology to confirm capture of the conduction system, etc.); (12) employing multiple EM coils (e.g., RFID tags, etc.) with catheter mechanical properties to interpolate the visualization of the entire catheter and its curvature that cannot be displayed by sensor data alone; (13) using non-contact cameras with optical (including photogrammetry), depth, and infrared sensors to monitor heart rate, blood pressure, respiratory rate, patient location/movement, physician/staff movement, and device tracking, etc.; (14) internally cross-validating location using catheter EM sensors (or RFID or echogenic surfaces), as well as external sources (e.g., if the physician feeds the catheter or rotates it externally, the internal sensors should move by the same ratio in response); (15) using device tracking to allow for verification of real time tracking and user tendencies; (16) feeding the biometrics and patient location and movement information into location and motion feedback loop for navigation through dynamic anatomy and/or physiology, as well as feeding using AI algorithms and patient sensor triplets (i.e., merging external and internal sensors with predictive modeling); (17) tracking in 3D optical on a 2D screen with haptic feedback (which is currently done on Hugo), to incorporate on cardiac, pulmonary, and/or other soft tissue luminal procedures; (17a) using head-mounted 3D views in 3D without a screen; (17b) using hand and gesture tracking on head-mounted XR displays to control robotic end-effectors, activate energy, and/or trigger automated instrument exchange; (17c) using XR headsets to visualize the surgical scene via optics (e.g., using the orientation of the headset, endoluminal cameras attached to the robot can be steered as the head rotates (with the system using inverse kinematics to map the input pose to desired robot arm joint angles); using corresponding input devices, paired with the headset, to control guide catheters and other auxiliary functions on the robot; etc.); (17d) using optics with navigation sensors to co-validate location and to allow for superimposing or merging multiple modalities; (17e) digitally displaying targets and trajectories in this coordinate system, as well as viewing anatomical movement (e.g., overlaying where adjacent structure like the coronary sinus, esophagus, and phrenic nerve are that cannot be seen optically alone); (17f) detecting perforation into the LV blood pool during fixation by pacing impedance to enable repositioning; (17g) accommodating delivery from multiple points of origin, such as the jugular, femoral vein/artery, transapical, etc.; (17h) adjusting views, data gathering, recommendations, and output for removal, adjustment, and reimplantation or redelivery of therapy; (17i) reconstructing in 3D surgical scenes that are viewed by the optics throughout the procedure and using these for post-op analysis, pre-op optimization, and clinical training (e.g., providing prediction and/or recommendation of battery and fatigue life of helix anchor from movement of leadless implant body cantilevered); (18) enabling the coordinate system (whether with robotics or not) to be tracked, replayed for manual analyses, or fed into the database for analyses and linkages to similar cases and acute/chronic outcomes; (18a) providing other features including, but not limited to, motion compensation, force feedback, force control, image guidance, and endoluminal catheter delivery; (19) displaying in the 3D view balloon pressure and sensor data in the preferred field of view of the medical professional, and using trigger sounds and images to alert on the 3D image and/or plots all at once to reduce cognitive load (i.e., to obviate the medical professional having to look back and forth among the console, patient, instruments, and navigation system); (20) enabling visualization of location contact or deformation before therapy, prediction of therapy shape and depth, tagging of locations (without having to inform an additional person on the map/nav console and looking away each time), recommendations on settings, location, contact force, and/or trajectory; (20a) enabling prediction via intra/post-op imaging and time-series data (and this may be modeled with FEA or CFD to feed into the digital twin); (20b) enabling visualization of therapy shapes and location on the digital twin; (21) enabling viewing and managing of variable measures from capital equipment and/or catheter sensors in XR field of view; (21a) enabling viewing, hearing, and control of warnings or cautions, visualization of predicted therapy, decisions such as start and stop of therapy, etc., in XR; (21b) enabling analysis of this time-series data (including, but not limited to, patient biometrics, therapy information, and/or chronic recurrence of arrhythmia, etc.) using machine learning; (21c) enabling feeding of these time-series analyses into a real-time application as data is being streamed and/or analyzed; (22) using image classification for auto-segmentation of imaging, identification of structures to ablate or avoid relative to previous morphology data, and providing structural change predictive modeling to the compensate for movement, posture, body fluid amount (e.g., dehydration), etc.; (22a) feeding a combination of these into a robotic compensating system; (22b) displaying any sensor data for barotrauma in XR and feeding this data into the AI feedback loop to recommend procedural changes; (22c) including real time fluoroscopy, biplanar fluoroscopy, and/or ventilator data/waveforms (Boyle&#39;s Law) in the image analyses and time series data; (22d) specifying a unique set of anatomy and describing the target lung tissue versus other structures, for the auto-segmentation and object recognition, and merging several image modalities to visualize smooth muscle, cartilage, gland, etc.; (23) aligning with features being built into Hugo (e.g., aligning with the Digital Surgery application that uses optical means of interfacing and AI algorithms to do actions like de-identifying faces and medical documents in the field of view; building in this functionality while adding imaging and EM navigation; etc.); (23a) enabling visualization in 3D with glasses and head track (and simulating eye gaze so if the medical professional looks away, a procedure cannot accidentally take place, in some cases using 3D image with 3D modality like a headset instead of a screen), haptic interactions with optical views, overlays of target and adjacent structures, etc.; (23b) using auto-stereoscopic 3D displays so users do not need to wear 3D glasses to operate the device (and using alternative approaches to head tracking, like gaze tracking from stereoscopic cameras, or using gaze inputs to control and/or activate certain features of the device (e.g., motion scaling, energy activation, and optical settings) in a hands-free mode; (23c) using image projectors, mobile device displays, monitors, and/or holographic displays to provide 3D visualization of the XR experience as overlays over the patient&#39;s body and/or as overlays over images of the patient&#39;s body (e.g., via image fusion, or the like); (24) enabling size reduction via AI enhancement and digital twins/XR; (24a) including use of a lower quality/smaller camera in the case that AI image enhancement is incorporated; (24b) reducing the number of sensors for navigation or contact sensing when using XR digital twins and algorithms to predict pathways and interpolate device/ablation shapes; (25) feeding data from use of different wavelengths or impedance to indicate lesion success into a virtuous data loop, and visualizing this data in XR; (25a) integrating data from optical, infrared, depth, sensors to create multiple layers of data for multi-mode cameras (e.g., HoloLens or Kinect cameras that have optical, infrared, depth, sensors, etc.); (26) including increased sensory perception data with visual XR cues and motion compensation, auditory cues, and with included haptics; and (26a) enabling adjusting of settings to give haptic, visual, and/or auditory feedback at different sensitivity levels for danger versus targeted areas; and/or the like. 
     The following detailed description illustrates a few exemplary embodiments in further detail to enable one of skill in the art to practice such embodiments. The described examples are provided for illustrative purposes and are not intended to limit the scope of the invention. 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the described embodiments. It will be apparent to one skilled in the art, however, that other embodiments of the present invention may be practiced without some of these specific details. In other instances, certain structures and devices are shown in block diagram form. Several embodiments are described herein, and while various features are ascribed to different embodiments, it should be appreciated that the features described with respect to one embodiment may be incorporated with other embodiments as well. By the same token, however, no single feature or features of any described embodiment should be considered essential to every embodiment of the invention, as other embodiments of the invention may omit such features. 
     Unless otherwise indicated, all numbers used herein to express quantities, dimensions, and so forth used should be understood as being modified in all instances by the term “about.” In this application, the use of the singular includes the plural unless specifically stated otherwise, and use of the terms “and” and “or” means “and/or” unless otherwise indicated. Moreover, the use of the term “including,” as well as other forms, such as “includes” and “included,” should be considered non-exclusive. Also, terms such as “element” or “component” encompass both elements and components comprising one unit and elements and components that comprise more than one unit, unless specifically stated otherwise. 
     Various embodiments described herein, while embodying (in some cases) software products, computer-performed methods, and/or computer systems, represent tangible, concrete improvements to existing technological areas, including, without limitation, medical operation technology, medical procedure technology, medical imaging technology, medical visualization and mapping technology, medical assistance technology, and/or the like. In other aspects, certain embodiments, can improve the functioning of user equipment or systems themselves (e.g., medical operation system, medical procedure system, medical imaging system, medical visualization and mapping system, medical assistance system, etc.), for example, by receiving, using a computing system, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room; receiving, using the computing system, one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices; analyzing, using the computing system, the received one or more first layer input data and the received one or more second layer input; generating, using the computing system, one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient, wherein the one or more soft tissue luminal portions comprise at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient; generating, using the computing system, one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations; and presenting, using the computing system and using a user experience (“UX”) device, the generated one or more XR images; and/or the like. 
     In particular, to the extent any abstract concepts are present in the various embodiments, those concepts can be implemented as described herein by devices, software, systems, and methods that involve specific novel functionality (e.g., steps or operations), such as, implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem that receives and combines the one or more first layer input data (i.e., room content-based data) and the one or more second layer input data (i.e., patient and/or tool-based data); that analyzes these data and generates recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis; that generates the one or more XR images; and that presents (using the UX device) the generated one or more XR images, and/or the like, to name a few examples, that extend beyond mere conventional computer processing operations. These functionalities can produce tangible results outside of the implementing computer system, including, merely by way of example, optimized and comprehensive IA or EI ecosystem that achieves better safety and efficacy (i.e., compensating for at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system and a flexible bronchoscope, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, as well as excessive movement of the user or physician, while reducing or eliminating the use of fluoroscopy and contrast, etc.), while reducing costs of operation of the system, increasing throughput of procedures, providing predictable procedure durations, reducing cognitive overload for the physician, increasing longevity of physician careers (e.g., by wearing lead during fluoroscopy), and/or the like, at least some of which may be observed or measured by users, patients, and/or service providers. 
     Various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device. 
     In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer). 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements. 
     Specific Exemplary Embodiments 
     We now turn to the embodiments as illustrated by the drawings.  FIGS.  1 - 9    illustrate some of the features of the method, system, and apparatus for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications, as referred to above. The methods, systems, and apparatuses illustrated by  FIGS.  1 - 9    refer to examples of different embodiments that include various components and steps, which can be considered alternatives or which can be used in conjunction with one another in the various embodiments. The description of the illustrated methods, systems, and apparatuses shown in  FIGS.  1 - 9    is provided for purposes of illustration and should not be considered to limit the scope of the different embodiments. 
     With reference to the figures,  FIG.  1    is a schematic diagram illustrating a system  100  for implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for soft tissue luminal applications, in accordance with various embodiments. Herein, the soft tissue luminal applications may include, but are not limited to, cardiovascular, pulmonary, neurovascular, or peripheral vascular applications, or the like. In some embodiments, related or corresponding soft tissue luminal procedures may include, without limitation, at least one of an atrioventricular dual chamber sensing and pacing procedure, a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), an endoluminal procedure, a cardiac endoluminal procedure, a pulmonary endoluminal procedure, a gastrointestinal endoluminal procedure, a neurovascular endoluminal procedure, a peripheral vascular endoluminal procedure, a surgical procedure, a left atrial appendage (“LAA”) procedure, a tissue ablation procedure, a transcatheter aortic valve repair (“TAVr”) procedure, a transcatheter aortic valve replacement (“TAVR”) procedure, a transcatheter mitral valve repair (“TMVr”) procedure, a transcatheter mitral valve replacement (“TMVR”) procedure, a transcatheter pulmonic valve repair (“TPVr”) procedure, a transcatheter pulmonic valve replacement (“TPVR”) procedure, a transcatheter tricuspid valve repair (“TTVr”) procedure, a transcatheter tricuspid valve replacement (“TTVR”) procedure, a mitral clip repair procedure, a shunt procedure, a coronary angioplasty procedure, a balloon angioplasty, a stenting procedure, an atrial septal defect (“ASD”) treatment procedure, a cardiac shunt treatment procedure, a heart bypass procedure, a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a catheter ablation procedure, an endovascular repair procedure, a heart monitor installation procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a minimally invasive endovascular repair procedure, a miniature leadless implant installation procedure, a miniature leadless pacemaker installation procedure, an implantable sensor installation procedure, a surgical heart valve repair and replacement procedure, a transcatheter pulmonary valve (“TPV”) therapy, a ventricular assist device (“VAD”) installation procedure, an intra-aortic balloon pump (“IABP”) implantation procedure, a heart transplant operation, a cryoballoon or cryoablation catheter procedure, a pulsed field ablation (“PFA”) procedure, an electroporation procedure, a radio frequency (“RF”) ablation procedure, a phased RF (“pRF”) ablation procedure, a microwave (“MW”) ablation procedure, a laser ablation procedure, a radiation ablation procedure, a microwave ablation procedure, a high intensity focused ultrasound (“HIFU”) procedure, a histotripsy procedure, an abdominal aortic aneurysm (“AAA”) procedure, a thoracic aortic aneurysm (“TAA”) procedure, a thoracoabdominal aortic aneurysm (“TAAA”) procedure, a complex aortic arch aneurysm procedure, a vascular occlusion procedure, an atherectomy procedure, a renal denervation procedure, a deep vein thrombosis (“DVT”) procedure, a thrombectomy procedure, a flow diversion endoluminal procedure, or a neuro stenting procedure, and/or the like. 
     In the non-limiting embodiment of  FIG.  1   , system  100  might comprise a system hub or computing system  105   a  and corresponding database(s)  110   a . In some cases, the database(s)  110   a  may be local to the system hub or computing system  105   a . In some cases, the database(s)  110   a  may be external, yet communicatively coupled to the system hub or computing system  105   a . In other cases, the database(s)  110   a  may be local and integrated within the system hub or computing system  105   a . System  100 , according to some embodiments, might further comprise mapping and navigation system  115   a  and corresponding database(s)  120   a . Like database(s)  110   a , the database(s)  120   a  may be local to the mapping and navigation system  115   a . In some cases, the database(s)  120   a  may be external, yet communicatively coupled to the mapping and navigation system  115   a . In other cases, the database(s)  120   a  may be local and integrated within the mapping and navigation system  115   a.    
     System  100  might include, without limitation, at least one of one or more healthcare professionals  125 , a subject  130 , one or more devices or equipment  135 , one or more imaging systems  140 , one or more sensors  145 , an extended reality (“XR”) platform or system  150 , a user experience (“UX”) device  155 , a data analytics or artificial intelligence (“AI”) system  160   a , or an anatomy or tool registration system  165 , and/or the like. In some instances, the system hub or computing system  105   a  and corresponding database(s)  110   a , the mapping and navigation system  115   a  and corresponding database(s)  120   a , and the at least one of the one or more healthcare professionals  125 , the subject  130 , the one or more devices or equipment  135 , the one or more imaging systems  140 , the one or more sensors  145 , the XR platform or system  150 , the UX device  155 , the data analytics or AI system  160   a , or the anatomy or tool registration system  165 , and/or the like, may be located or disposed within clinical environment  170 . In some cases, the clinical environment  170  might include, but is not limited to, a clinic, a hospital, an operating room, an emergency room, a physician&#39;s office, or a laboratory, or the like. 
     In some embodiments, the system hub or computing system  105   a  might include, without limitation, at least one of an XR computing system, a medical procedure computing system, a hub computing system, a three-dimensional (“3D”) graphical processing unit, a cluster computing system, a four-dimensional (“4D”) graphics computing system, a server computer, a cloud computing system, or a distributed computing system, and/or the like. In some instances, the one or more healthcare professionals  125  might include, without limitation, at least one of one or more doctors, one or more surgeons, one or more cardiologists, one or more electrophysiologists, one or more cardiac surgeons, one or more neurosurgeons, one or more radiologists, one or more scenographers, one or more nurse practitioners, one or more nurses, one or more medical specialists, one or more medical imaging specialists, and/or the like. In some cases, the subject  130  might include, but is not limited to, one of a human patient; a large animal (e.g., pig, sheep, dog, etc.); a small animal (e.g., rabbit, rat, mouse, etc.); an organ (e.g., explant, transplant, decellularized, deceased, generated, synthetic, etc.); an organelle; one or more organs on a chip; one or more tissue constructs; one or more cells; one or more microbes of bacterial vectors; one or more microbes of viral vectors; one or more microbes of prion vectors; one or more genes, deoxyribonucleic acid (“DNA”), ribonucleic acid (“RNA”); one or more hormones, one or more biochemicals, one or more molecules; one or more tissues, one or more blood vessels, or one or more bones; and/or the like. 
     According to some embodiments, the one or more devices or equipment  135 —which might include surgical tool(s)/device(s)  135   a , implantable device(s)  135   b , or the like—might include, but is not limited, at least one of one or more catheters, one or more catheter interconnect cables, one or more valves, one or more balloons, one or more leads (e.g., pacemaker or defibrillator leads, etc.), one or more rigid robotic devices (e.g., one or more soft robotic devices, one or more robotic systems, one or more robotic arms, one or more handheld robotic systems, one or more robotic systems integrated into a device handle, etc.), one or more stents, one or more needles, one or more grafts, one or more occluders, one or more shunts, one or more therapeutic delivery devices, one or more implant delivery devices, one or more diagnostic devices, one or more diagnostic catheters, one or more bronchoscopes, one or more implantable devices, one or more surgical tools, one or more biopsy tools, one or more excision tools, one or more ablation catheters (e.g., a cryocatheter, a cryoballoon, a pulse field ablation (“PFA”) catheter, an radio frequency (“RF”) ablation catheter, a phased RF (“pRF”) ablation catheter, a laser ablation catheter, a radiation or microwave catheter, an ultrasonic ablation catheter, etc.) and corresponding systems, one or more imaging tools, one or more fiducials, one or more staples, one or more anchors, one or more meshes, one or more vascular cannulae, one or more circulatory pumps, one or more valve repair or replacement devices (e.g., aortic, mitral, tricuspid, or pulmonary valve repair or replacement devices, etc.), one or more embolic protection devices, one or more vascular closure tools, one or more septal closure tools, one or more ventricular closure tools, one or more lasers, one or more plaque removal tools, one or more guide wires, one or more introducers, one or more sheaths, one or more PillCams, one or more clips, one or more capsules, a miniature leadless implant, a miniature leadless pacemaker delivery system, a miniature leadless pacemaker, and/or the like. The one or more devices or equipment  135  might be configured to perform one or more tasks. 
     In some embodiments, the one or more tasks might include, without limitation, at least one of a surgical procedure, an atrioventricular dual chamber sensing and pacing procedure, a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), an endoluminal procedure, a cardiac endoluminal procedure, a pulmonary endoluminal procedure, a gastrointestinal endoluminal procedure, a neurovascular endoluminal procedure, a peripheral vascular endoluminal procedure, a surgical procedure, a left atrial appendage (“LAA”) procedure, a tissue ablation procedure, a transcatheter aortic valve repair (“TAVr”) procedure, a transcatheter aortic valve replacement (“TAVR”) procedure, a transcatheter mitral valve repair (“TMVr”) procedure, a transcatheter mitral valve replacement (“TMVR”) procedure, a transcatheter pulmonic valve repair (“TPVr”) procedure, a transcatheter pulmonic valve replacement (“TPVR”) procedure, a transcatheter tricuspid valve repair (“TTVr”) procedure, a transcatheter tricuspid valve replacement (“TTVR”) procedure, a mitral clip repair procedure, a shunt procedure, a coronary angioplasty procedure, a balloon angioplasty, a stenting procedure, an atrial septal defect (“ASD”) treatment procedure, a cardiac shunt treatment procedure, a heart bypass procedure, a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a catheter ablation procedure, an endovascular repair procedure, a heart monitor installation procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a minimally invasive endovascular repair procedure, a miniature leadless implant installation procedure, a miniature leadless pacemaker installation procedure, an implantable sensor installation procedure, a surgical heart valve repair and replacement procedure, a transcatheter pulmonary valve (“TPV”) therapy, a ventricular assist device (“VAD”) installation procedure, an intra-aortic balloon pump (“IABP”) implantation procedure, a heart transplant operation, a cryoballoon or cryoablation catheter procedure, a pulsed field ablation (“PFA”) procedure, an electroporation procedure, a radio frequency (“RF”) ablation procedure, a phased RF (“pRF”) ablation procedure, a microwave (“MW”) ablation procedure, a laser ablation procedure, a radiation ablation procedure, a microwave ablation procedure, a high intensity focused ultrasound (“HIFU”) procedure, a histotripsy procedure, an abdominal aortic aneurysm (“AAA”) procedure, a thoracic aortic aneurysm (“TAA”) procedure, a thoracoabdominal aortic aneurysm (“TAAA”) procedure, a complex aortic arch aneurysm procedure, a vascular occlusion procedure, an atherectomy procedure, a renal denervation procedure, a deep vein thrombosis (“DVT”) procedure, a thrombectomy procedure, a flow diversion endoluminal procedure, or a neuro stenting procedure, and/or the like. In some instances, the atrioventricular dual chamber sensing and pacing procedure may include, but is not limited to, a procedure that utilizes one of a leadless pacemaker or a lead pacemaker (or pacemaker with a lead) and/or a procedure that involves one of a device that is positioned in one chamber of the heart while enabling sensing and pacing across two chambers or a device that is positioned in two chambers of the heart while enabling sensing and pacing across these two chambers, and/or the like. In some cases, the neurovascular endoluminal procedure may include, without limitation, stenting, balloon angioplasty, and/or the like, in the brain, while the peripheral vascular endoluminal procedure may include, but is not limited to, stenting, balloon angioplasty, rotablation (e.g., ablation using a very small drill bit at the tip of a special catheter), and/or the like, in the extremities of the body. 
     According to some embodiments, the one or more imaging devices or systems  140  might include, but is not limited to, at least one of a magnetic resonance imaging (“MRI”) system, a diffusion-tensor imaging (“DTI”) system, a computed tomography (“CT”) system, an intraoperative 2D/3D imaging system (“O-Arm”), an ultrasound (“US”) system (including 2D, 3D, or 4D US, or the like), a transesophageal echocardiography (“TEE”) system, an intra-cardiac echocardiography (“ICE”) system, a transthoracic echocardiography (“TTE”) system, an intravascular ultrasound (“IVUS”) system, an endobronchial ultrasound system (“EBUS”), an endoscopic ultrasound system (“EUS”), an electromechanical wave imaging (“EWI”) system, a neuro-endoscopy system, a single photon emission computed tomography (“SPECT”) system, a magnetic resonance angiography (“MRA”) system, a computed tomography angiography (“CTA”) system, a blood oxygen-level dependent signal (“BOLD”) system, an arterial spin labeling (“ASL”) system, a magnetoencephalography (“MEG”) system, a positron emission tomography (“PET”) system, an electroencephalography (“EEG”) system, an optical coherence tomography (“OCT”) system, an optical imaging spectroscopy (“OIS”) system, a magnetic resonance spectroscopy (“MRS”) system, a dynamic susceptibility contrast (“DSC”) MRI system, a fluid-attenuated inversion recovery (“FLAIR”) system, a fluoroscopy system, a biplane fluoroscopic or cineradiographic system, a rotational angiographic system, an X-ray system, a 3D scanning system, an infrared (“IR”) system, an ultraviolet (“UV”) system, a bioluminescent system, an endoscopy system, a triboluminescence system, an image fusion system, a borescope, a video camera, a PillCam, or a microscope, and/or the like. 
     In some embodiments, the one or more sensors  145  might include, without limitation, at least one of one or more chronically implanted sensors, one or more diagnostic sensors, one or more surgical sensors, one or more wearable sensors, one or more gas sensors, one or more optical sensors, one or more impedance sensors, one or more ultrasound sensors, one or more flow sensors, one or more blood velocity sensors, one or more blood volume sensors, one or more electrical sensors, one or more voltage sensors, one or more amperage sensors, one or more wattage sensors, one or more motion sensors, one or more sound sensors, one or more blood pressure sensors, one or more heart rate sensors, one or more pulse sensors, one or more oxygen sensors, one or more carbon dioxide (“CO 2 ”) sensors, one or more fluid levels, one or more lung volume sensors, one or more tidal volume sensors, one or more lung filling pressure sensors, a pulmonary artery pressure sensor, one or more piezoelectric sensors, one or more accelerometers, one or more image sensors, one or more acoustic sensors, one or more temperature sensors, one or more ambulatory monitoring sensors, one or more patient weight sensors, one or more patient mattress sensors, one or more doppler sensors, one or more biomarker sensors, one or more perfusion sensors, one or more electromyography (“EMG”) sensors, one or more electrocardiography (“ECG”) sensors, one or more electromechanical wave imaging (“EWI”) system sensors, one or more electroanatomic mapping (“EAM”) system sensors, one or more sleep sensors, one or more cardiac hemodynamics sensors, one or more ischemia sensors, one or more hematocrit (“HCT”) level sensors, one or more biometric sensors, one or more electroencephalographic (“EEG”) sensors, one or more apnea monitoring sensors, one or more dyspnea monitoring sensors, one or more nociception monitoring sensors, or one or more pain sensors, and/or the like. 
     According to some embodiments, the XR platform or system  150  might include, without limitation, at least one of an XR headset, a set of XR goggles, a pair of XR-enabled eyewear, an XR-enabled smartphone mounted in a headset, an XR helmet, a mixed reality (“MR”) headset, a set of MR goggles, a pair of MR-enabled eyewear, an MR-enabled smartphone mounted in a headset, an MR helmet, a virtual reality (“VR”) headset, a set of VR goggles, a pair of VR-enabled eyewear, an VR-enabled smartphone mounted in a headset, an VR helmet, an augmented reality (“AR”) headset, a set of AR goggles, a pair of AR-enabled eyewear, an AR-enabled smartphone mounted in a headset, or an AR helmet, and/or the like. Herein, VR might refer to a simulated experience that uses fully virtual constructs generated by a computing system or the like, while AR might refer to an interactive experience of a real-world environment where objects in the real-world are enhanced or augmented by computer-generated perceptual information (in some cases, including visual, auditory, haptic, somatosensory, and/or olfactory information). MR might refer to a merging of the real and virtual worlds to produce new environments and visualizations in which physical and virtual objects co-exist and interact in real-time (in some cases, MR might include AR plus physical interaction and information from the environment that goes beyond just visual aspects, or the like). XR might refer to real and virtual combined environments and human-machine interactions generated by a computing system or the like, and includes AR, MR, and/or VR. 
     In some instances, the XR platform or system  150  might generate one or more XR experiences including, but not limited to, at least three or more of the one or more XR images, one or more XR sounds, one or more XR haptic or tactile responses, one or more XR simulated smells, or one or more XR simulated tastes, and/or the like, in some cases, based at least in part on the mapping performed by the mapping and navigation system  115   a . In some instances, the mapping and navigation system  115   a  might include, but is not limited to, at least one of an electroanatomic mapping (“EAM”) system, an electromagnetic (“EM”) mapping and/or navigation system, a radiofrequency identification (“RFID”) mapping and/or navigation system, an impedance-based mapping and/or navigation system, an ultrasound (“US”) mapping and/or navigation system, an optical mapping and/or navigation system, a high-density mapping catheter (e.g., Achieve™ mapping catheter, Achieve Advance™ mapping catheter, Marinr™ CS mapping catheter, Marinr™ MC mapping catheter, Marinr™ MCXL mapping catheter, Marinr™ SC mapping catheter, StableMapr™ mapping catheter, or the like), one or more patient patches, or navigation hardware and software, and/or the like. 
     In some embodiments, the UX device  155  might include, without limitation, at least one of a headset, UX glasses, a viewing window, a supplement to existing glasses, headphones, UX contact lenses, a heads-up display (“HUD”) device, a 3D spatial sound system, a telemonitoring system, a rigid robotic device control and sensory feedback system, a soft robotic device control and sensory feedback system, an eye control system, a voice control system, a remote control system, a gesture-based control system, a sign language-based control system, a body-part-based control system, a joystick, a mouse, a two-dimensional (“2D”) screen display, a 3D refractive display, a parallel reality system, a projection system, a 3D printed reconstruction system, a customized view generation system, a ghosting and prediction system, a master-slave control system, an annotation system, or a haptic feedback system, and/or the like. Merely by way of example, in some cases, VR images may be generated using the UX device  155  to enable a remote user to control a robot or robotic system (that is located near the patient, either in another room or in another city, region, state/province, country, etc. as the user). In some instances, AR image overlays may be generated for in person manually controlled procedures or for in person procedures utilizing robotic systems. According to some embodiments, holographic user interfaces (“UIs”) and/or holographic UXs may be used to enable control of one or more aspects of a surgical device or instrument (e.g., zoom, brightness, and/or contrast of an endoscope, etc.) and/or displaying, to the user, key data (e.g., warnings, alarms, and/or notifications, etc.) from a robot, and/or the like. In some embodiments, the system may generate and display (using the UX device  155 ) projected or predicted position(s) (or real-time or adjusted) position(s) of deployed devices (e.g., heart valves, pacing defibrillator leads or devices, leadless implant devices, leadless pacemakers, delivery systems, etc.) and/or therapeutic effects (e.g., ablation lesions and/or zones, etc.) with respect to the real-time location and/or configuration of delivery devices and/or therapy devices, and/or the like. 
     Merely by way of example, in some cases, alternative or additional to the system hub or computing system  105   a  and corresponding database  110   a , the mapping and navigation system  115   a  and corresponding database  120   a , and/or data analytics or AI system  160   a  being disposed within clinical environment  170 , system  100  might comprise remote system hub or computing system  105   b  and corresponding database(s)  110   b , remote mapping and navigation system  115   b  and corresponding database(s)  120   b , and/or data analytics or AI system  160   b  that communicatively couple with the system hub or computing system  105   a  (or communications system (not shown)) disposed within the clinical environment  170  via one or more networks  175 . According to some embodiments, system  100  might further comprise (optional) cloud storage  180 , which communicatively couples with the system hub or computing system  105   a  via the one or more networks  175 . Merely by way of example, network(s)  175  might each include a local area network (“LAN”), including, without limitation, a fiber network, an Ethernet network, a Token-Ring™ network, and/or the like; a wide-area network (“WAN”); a wireless wide area network (“WWAN”); a virtual network, such as a virtual private network (“VPN”); the Internet; an intranet; an extranet; a public switched telephone network (“PSTN”); an infra-red network; a wireless network, including, without limitation, a network operating under any of the IEEE 802.11 suite of protocols, the Bluetooth™ protocol known in the art, and/or any other wireless protocol; and/or any combination of these and/or other networks. In a particular embodiment, network(s)  175  might each include an access network of an Internet service provider (“ISP”). In another embodiment, network(s)  175  might each include a core network of the ISP, and/or the Internet. 
     According to some embodiments, one or more catheter interconnect or interface cables may be used. In some instances, the one or more catheter interconnect or interface cables might include a chip or memory device that is used to store, collect, and transfer data for the XR database. The chip or memory device may also be used to authenticate the device (e.g., as being compatible with the system or as being procedure-qualified, or the like), and may include security features that, when enabled, prevents information from being read or written. For single use devices, this chip or memory device can limit the number of uses to 1. In this manner, the catheter interconnect or interface cables may be used to meet business and/or healthcare requirements: (1) to restrict to single use of a device; (2) to authenticate the device as a real, approved device; (3) to secure the data stored on the device for access by only authorized users; and/or the like. In addition, the catheter interconnect or interface cables may also be used to achieve future additional business and/or healthcare requirements, including, but not limited to: (4) storing, collecting, and/or transferring data for XR applications; and/or the like. To incorporate a chip or memory device into a catheter, the chip or memory device might be mounted on a printed circuit board (“PCB”), which could include other hardware to enable features including, but not limited to: device or procedure sensing (e.g., temperature, orientation, acceleration, position, pressure, humidity, audio record, etc.); wireless communication (e.g., Bluetooth™, network, RFID, etc.); manufacturing and/or device history data storage and transfer to XR information database; and/or the like. 
     In some aspects, the IA or EI ecosystem, as represented by  FIG.  1    for example, might achieve one or more of the following features or functionalities: (1) fast visualization to shorten procedure time; (2) less recurrence for optimal patient outcomes; (3) lower healthcare utilization costs and lower capital equipment costs; (4) flexibility; and/or (5) fluoroless implementation; or the like. To achieve fast visualization, the IA or EI ecosystem might implement one or more of the following: identify targets in seconds or minutes, maintain occlusion and contact, and/or titrate dosage to reduce number of ablations (thus shortening procedure time), or the like. To achieve less recurrence, the IA or EI ecosystem might implement one or more of the following: utilize powerful predictive analytics in comparison with existing visualization systems, visualize and personalize to patient-specific anatomy, implement multiple checkpoints during each procedure to ensure best patient outcomes, and/or collect occlusion assessment and lesion tagging data, or the like. To achieve lower costs, the IA or EI ecosystem might implement one or more of the following: avoid using expensive capital equipment and/or utilizing MR or XR, which is more than 70% less costly than existing mapping and visualization systems used in arrhythmia treatment or the like. To achieve flexibility, the IA or EI ecosystem might implement one or more of the following: utilize an open system that uses MR or XR with any energy source and/or implement remote sales operation (to achieve geographic flexibility in hard-to-reach locations), or the like. To achieve fluoroless implementation, the IA or EI ecosystem might implement one or more of the following: avoid radiation exposure and/or achieve high resolution imaging of internal cardiac, pulmonary, or other lumen structures without exposing patients to radiation, or the like. Alternatively, or additionally, the IA or EI ecosystem, as represented by  FIG.  1    for example, may achieve one or more of the following features or functionalities: (6) improved therapy delivery accuracy for intended targets for better efficacy; (7) reduction in adjacent structure damage; (8) reduced cognitive load for the healthcare professional; (9) reduced exposure to radiation for the patient and/or healthcare professional; (10) simulated patient dye for target organs or tissue; (11) reduction in the number of people needed due to increased control by the user or operator; (12) improved telementoring; (13) improved telemedicine; and/or (14) enables social distancing or total separation (to address pandemic-related issues, such as COVID-19 issues, or the like). 
     To implement the IA or EI ecosystem, once the anatomy can be visualized and the location of therapy delivery can be navigated to, the choice of device(s) and how to control the device(s) is the next building block. It may be a robotic system like the Hugo/Einstein for soft tissues (Mazor for bone), catheters, delivery systems, surgical tools, etc., such as described above with respect to the one or more devices or equipment  135 , or the like. For a physician or healthcare professional (such as healthcare professionals  125 , or the like) to have real time actionable data, sensors (such as sensors  145 , or the like) need to be employed in the system on the patient (e.g., subject  130 , or the like), provider, tools, and equipment (e.g., devices or equipment  135 , or the like). For example, the visualization tool such as XR hardware (including, but not limited to, Microsoft HoloLens®, or the like) might have several cameras and sensors (not only for visualization) to measure key biometrics in a non-contact manner. In some instances, the visualization tool such as XR hardware may utilize photogrammetry for calibration and/or fiducials (i.e., markers or objects placed in a field of view or imaging system for use as a point of reference or measure, or the like). Depending on the procedure and the need, there may be several sensors that can be employed, for example, eye gazing on the Hugo robot might shut down the system to avoid inadvertent movement or injury, which could be employed via the HoloLens headset on any therapy delivery (including, without limitation, TAVR, TMVR, cardiac implant, etc.). 
     Now that the sensors have gathered the data, the data must be processed for use by the physician or healthcare professional. For instance, a general workflow for processing the data might include the following: (i) problem definition (including, but not limited to, objectives, hypotheses, measurement, cohorts, and/or end points, or the like); (ii) data collection (including, but not limited to, access, transfer, governance, and/or storage of data including, without limitation, internal/external data, historical data, batch data, streaming data, and/or log data, or the like); (iii) data curation (including, but not limited to, quality, cleaning, merging, segmenting, and/or transforming, or the like); (iv) model building (including, but not limited to, features, test models, test analytics, and/or validation, or the like); (v) analysis (including, but not limited to, exploring, analyzing, adjusting, and/or repeating one or more of data mining, AI machine learning or deep learning, statistical analysis, and/or natural language processing, or the like); (vi) visualization (including, but not limited to, graphical, tabular, and/or dashboard visualization of real-time, near-real-time, and/or aggregate data, or the like); (vii) insight and action (including, but not limited to, trends, what, why, and/or how, or the like); and (viii) follow-up (including, but not limited to, prescribing follow-up and long-term monitoring, or the like). Such general workflow may be used to process the three V&#39;s of big data—namely, volume (including, without limitation, health records, insurance, transactions, and/or mobile sensors, or the like), velocity (including, without limitation, batch, near-real-time, real-time, and/or streaming, or the like), and variety (including, without limitation, structured, unstructured, semi-structured, and/or the like). 
     The types of data, sources, and processing methods or analytics might include, but are not limited to, auto-segmentation; geometric analyses; device stabilization and filtering; algorithms; anomalies; outliers; trends over time; image identification or recognition; mobile sensors; measures and prediction for custom, group, etc. (e.g., procedural times, cost, fluoroscopy use, contrast use, team performances, or the like); device acute or chronic performance prediction (e.g. rhythm prediction before and during ablation, or the like); reimbursement or insurance analytics or treatment; health records; transactions; prescriptive modeling; predictive modeling; forecasting or extrapolation; diagnostic or statistical analyses; dashboards or alerts; query or drilldown; Ad hoc reports; standard reports; Internet of Things (“IoT”); data mining; and/or the like. Alternatively, or additionally, the types of data, sources, and processing methods or analytics might include, without limitation, structured; unstructured; semi-structured; multi-device factors; multi-comorbidity factors; data analytics; data privacy; data science; data visualization; simulations; predictions; recommendations; probability of success and adverse events; precise and personalized care; optimizing therapy delivery; evidence based medicine; value-based healthcare (“VBHC”); predictive analytics; prescriptive analytics; care management and real-time patient monitoring; computer aided detection (“CADe”); computer aided diagnosis (“CADx”); medical image processing; device feedback; subject feedback; demographics, global, regional, local, racial, social, familial, diet, mental, emotional, spiritual, attitudinal, genetic, lifestyle, insurance, economic factors, or the like; pre-procedural; intraprocedural; post-procedural; chronic; and/or the like. 
     In a specific, non-limiting example data use case (i.e., utilizing the HoloLens or the like), goals of a solution architecture for analytics and machine learning might include, but are not limited to: telemetry capture (including, without limitation, 3D positioning of a catheter in real-time, procedure duration and ablation accuracy, heart rhythm, electrical signal reduction, scarred or destroyed tissue, and other vitals, or the like); providing for retrospective analytics (including, without limitation, analyzing individual and arbitrary aggregations of procedures on an ad hoc basis, answering common questions to drive data-driven improvements to procedure (e.g., “how much time is spent in various areas of the heart, the lung, a blood vessel, a GI tract, or another lumen?” and “what was the accuracy and outcome of the procedure?” or the like)); machine learning integration (including, without limitation, real-time and offline or batch, support proposed use cases (e.g., providing real-time prediction of impact that the procedure has had on electrical signal and prognosis; providing real-time estimate of tissue scarred or destroyed, including percentage considered “in excess”; providing information regarding depth, width, and/or permanency of tissue damage or destruction (e.g., some ablation types like reversible (compared with irreversible) electroporation actually open up cell walls to all for drugs to enter then heal and close up, or the like)); providing real-time anomaly detection of vitals, including dips, peaks, and long-term trend variance; recommending patient-specific ablation locations to reduce probability of repeat surgery; recommending path optimization for procedure based on patient-specific anatomy; or the like)). 
     With so many data sources, the packaging of display of these into a user interface (such as a UX device, or the like) to only have the right information, at the right time, and in the right place needs to be done to minimize cognitive overload. Although we have shown 3D screen and 3D headset examples, several UX types and feedback loops that can be employed are as described above with respect to UX device  155 . All the parts of the system need to communicate in a seamless manner to be useful in real time. The parts of a non-limiting XR or IA/EI ecosystem, according to some embodiments, might include, without limitation, headset; tethered unit; cloud; data warehouse; data lake; computer processor; and/or the like. Lastly, the application of the ecosystem can be deployed on various subjects (as described above with respect to subject  130 ). 
     In many aspects, system  100  may provide IA or EI ecosystem functionality for any number of tasks, such as those described in detail in the &#39;278, &#39;283, &#39;289, and &#39;632 Applications (i.e., the “Related Applications”), which have already been incorporated herein by reference in their entirety for all purposes. The various embodiments described herein focus on those tasks associated with soft tissue luminal applications including, but not limited to, at least one of an atrioventricular dual chamber sensing and pacing procedure, a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), an endoluminal procedure, a cardiac endoluminal procedure, a pulmonary endoluminal procedure, a gastrointestinal endoluminal procedure, a neurovascular endoluminal procedure, a peripheral vascular endoluminal procedure, a surgical procedure, a left atrial appendage (“LAA”) procedure, a tissue ablation procedure, a transcatheter aortic valve repair (“TAVr”) procedure, a transcatheter aortic valve replacement (“TAVR”) procedure, a transcatheter mitral valve repair (“TMVr”) procedure, a transcatheter mitral valve replacement (“TMVR”) procedure, a transcatheter pulmonic valve repair (“TPVr”) procedure, a transcatheter pulmonic valve replacement (“TPVR”) procedure, a transcatheter tricuspid valve repair (“TTVr”) procedure, a transcatheter tricuspid valve replacement (“TTVR”) procedure, a mitral clip repair procedure, a shunt procedure, a coronary angioplasty procedure, a balloon angioplasty, a stenting procedure, an atrial septal defect (“ASD”) treatment procedure, a cardiac shunt treatment procedure, a heart bypass procedure, a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a catheter ablation procedure, an endovascular repair procedure, a heart monitor installation procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a minimally invasive endovascular repair procedure, a miniature leadless implant installation procedure, a miniature leadless pacemaker installation procedure, an implantable sensor installation procedure, a surgical heart valve repair and replacement procedure, a transcatheter pulmonary valve (“TPV”) therapy, a ventricular assist device (“VAD”) installation procedure, an intra-aortic balloon pump (“IABP”) implantation procedure, a heart transplant operation, a cryoballoon or cryoablation catheter procedure, a pulsed field ablation (“PFA”) procedure, an electroporation procedure, a radio frequency (“RF”) ablation procedure, a phased RF (“pRF”) ablation procedure, a microwave (“MW”) ablation procedure, a laser ablation procedure, a radiation ablation procedure, a microwave ablation procedure, a high intensity focused ultrasound (“HIFU”) procedure, a histotripsy procedure, an abdominal aortic aneurysm (“AAA”) procedure, a thoracic aortic aneurysm (“TAA”) procedure, a thoracoabdominal aortic aneurysm (“TAAA”) procedure, a complex aortic arch aneurysm procedure, a vascular occlusion procedure, an atherectomy procedure, a renal denervation procedure, a deep vein thrombosis (“DVT”) procedure, a thrombectomy procedure, a flow diversion endoluminal procedure, or a neuro stenting procedure, and/or the like. 
     In such operations, system hub or computing system  105   a  or  105   b  (collectively, “computing system” or the like) might receive one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons (e.g., subject  130 , healthcare professional(s)  125 , etc.) and one or more objects (e.g., device(s) or equipment  135 , furniture, etc.) within a room (i.e., clinical environment  170 , or the like). In such cases, the one or more first devices may include at least one of imaging system(s)  140 , sensor(s)  145 , and/or the like, that are configured to obtain, capture, or otherwise provide the one or more first layer input data. The computing system might receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient (i.e., subject  130 ), one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices (i.e., the devices or equipment  135 , or the like) relative to the one or more portions of the body of the patient, and/or the like. In such cases, the one or more second devices may include at least one of device(s) or equipment  135 , imaging system(s)  140 , sensor(s)  145 , and/or the like, that are configured to obtain, capture, or otherwise provide the one or more second layer input data. 
     The computing system might analyze the received one or more first layer input data and the received one or more second layer input. The computing system might generate one or more recommendations for guiding a medical professional (i.e., healthcare professional(s)  125 , or the like) in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient. In some embodiments, the one or more soft tissue luminal portions may include, but are not limited to, at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient, and/or the like. In some instances, the lumen may include, without limitation, one of the heart, the lung, the blood vessel (e.g., an artery, a vein, or a capillary, etc.), the gastrointestinal tract (e.g., the esophagus, the stomach, the small intestine, or the large intestine, etc.), a respiratory tract (e.g., the trachea, bronchi of the lungs, etc.), neurovascular (e.g., artery or vein in the brain, etc.), peripheral vascular (e.g., artery or vein in the extremities, etc.), or a female genital tract, and/or the like. The computing system might then generate one or more XR images (or one or more XR experiences), based at least in part on the generated one or more recommendations, and might present the generated one or more XR images (or one or more XR experiences) using a UX device  155 . According to some embodiments, the one or more XR images might be dynamic images, which might include an overlay of data models depicting at least one of electrical pulses, blood flow, tissue movement, damage, stress, and/or the like, and thus may not be a still frame in 3D. In some embodiments, the one or more XR images might include, without limitation, at least one of one or more AR images, one or more AR videos, one or more VR images, one or more VR videos, one or more MR images, one or more MR videos, one or more XR images, or one or more XR experiences (also referred to as “XR scenes” or “XR applications”; which includes components including, but not limited to, XR coding, XR images, XR videos, XR physics, and/or the like), and/or the like. 
     According to some embodiments, the generated one or more XR images might be presented to provide one or more of: a guide for a medical professional (e.g., healthcare professional(s)  125 , or the like), a navigation tool during the soft tissue luminal procedure, a proximity detection tool during the soft tissue luminal procedure, a 3D or 4D visualization view of the at least one or more portions of the patient, a 3D or 4D visualization view of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of at least one of the one or more first layer input data, a heads-up display of at least one of the one or more patient sensor data, a heads-up display of at least one of the one or more patient imaging data, a heads-up display of physiological data of the patient, or a heads-up display of procedure-related data of the patient, and/or the like. Herein, “a digital twin” may refer to a virtual representation of an objection or a system that spans its lifecycles, is updated from real-time data, and uses simulation, machine learning, and reasoning to assist in decision-making. In some cases, connected sensors on the physical asset (e.g., tool, device, or system, or the like) may collect data that can be mapped onto the virtual model (i.e., the digital twin). In some instances, generating the one or more XR images might comprise combining or mapping the received one or more first layer input data and the received one or more second layer input into a combined 3D or 4D representation, based at least in part on the analysis and the generated one or more recommendations; and generating the one or more XR images based on the combined 3D or 4D representation. 
     In some embodiments, the computing system might track the one or more surgical devices (e.g., devices or equipment  135 , or the like), using at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system, and/or the like. 
     According to some embodiments, the computing system might receive one or more control inputs from the medical professional; might analyze the received one or more control inputs in conjunction with analysis of the received one or more first layer input data and the received one or more second layer input data; might generate one or more control instructions based at least in part on the analysis, the generated one or more control instructions taking into account movement including at least one of movement of one or more soft tissue luminal portions and surrounding tissue due to at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, and/or the like; and might send the generated one or more control instructions to the robotic system (which may, in some cases, be included among the device(s) or equipment  135 , or more specifically among the surgical tool(s) or device(s)  135   a , or the like) to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient. 
     In some instances, at least the processes of receiving the one or more first layer input data, receiving the one or more second layer input data, analyzing the received one or more first layer input data and the received one or more second layer input, generating the one or more recommendations, generating the one or more XR images, presenting the generated one or more XR images, receiving the one or more control inputs, analyzing the received one or more control inputs, generating the one or more control instructions, and/or sending the generated one or more control instructions may occur in a manner that is at least one of continual, dynamic, feedback-looped, updated, in real-time, or in near-real-time, and/or the like, during the course of the soft tissue luminal procedure. 
     In some embodiments, the received one or more control inputs may comprise hand-movement-based control inputs resulting from movement of one or more hands of the medical professional. In such cases, analyzing the received one or more control inputs may comprise determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional. As such, generating the one or more control instructions may comprise, based on a determination that the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the medical professional, the computing system generating one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more particular control inputs that are based on excessive movement of the at least one hand of the medical professional. In some cases, using robotic placement, movement, and/or collision detection, the excessive movement of the at least one hand of the medical professional may be tracked relative to the robotic arm or relative to an implant device or catheter, with the computing system generating the one or more compensated control instructions accordingly. 
     According to some embodiments, the data processing and computation may be performed locally (e.g., at system hub/computing system  105   a , mapping and navigation system  115   a , XR platform/system  150 , and/or data analytics/AI system  160   a , or the like), may be performed at a remote system(s) (e.g., at system hub/computing system  105   b , mapping and navigation system  115   b , data analytics/AI system  160   b , another server(s), and/or a cloud computing-based system, or the like), or may be performed using a combination of local and/or remote systems, or the like. 
     In some aspects, the IA or EI ecosystem may utilize AI-assisted image processing for determining an appropriate target site, image- and/or sensor-based catheter deployment (e.g., delivery system or catheter deployment, etc.) by a robotic machine, near-instant signal processing to confirm the success of the catheter deployment, ensuring complete connection to the catheter, confirming catheter positioning and location, and/or the like, all performed autonomously (or with minimal if any human input). In some embodiments, the procedure may be performed without fluoroscopy. 
     These and other functions of the system  100  (and its components) are described in greater detail below with respect to  FIGS.  2 - 7   . 
       FIG.  2    is a schematic diagram illustrating a non-limiting example  200  of building blocks for an IA or EI ecosystem that may be implemented for soft tissue luminal applications, in accordance with various embodiments. 
     With reference to the non-limiting example  200  of  FIG.  2   , the IA or EI ecosystem  200  might comprise a system hub or aggregator (block  205 ) and a plurality of components or building blocks of the IA or EI ecosystem (blocks  210 - 275 ) that are communicatively coupled with the system hub or aggregator (at block  205 ). According to some embodiments, the IA or EI ecosystem  200  may further include a programmer system  205   a , which may be used to program one or more settings or configurations of a device(s) (e.g., a catheter, a miniature leadless implant, a pacemaker or pacer, a delivery system, an endoluminal robot, etc.). In some instances, the one or more settings or configurations of the miniature leadless device may include, but are not limited to, at least one of pacing mode, rate limits, stimulation parameters, sensing parameters, rate response parameters, or other parameters related to operation of the miniature leadless device, and/or the like. In some embodiments, the programmer system may also be used to enter patient information, including, but not limited to, at least one of name, identification (“ID”), date of birth, history of clinical conditions, device serial number, physician notes, physician name, physician phone number, or the hospital where the implant occurred, and/or the like. This information may be used to generate a Patient Information Report, which may be entered into electronic health records (“EHR”) or the like. According to some embodiments, the initial device operating parameters—including, but not limited to, at least one of R-wave amplitude in mV, electrode impedance in ohms, the pacing threshold in V, and the stimulation pulse width in msec, and/or the like—may be sent from the device to the programmer system. 
     In some embodiments, the plurality of components might include, without limitation, at least one of one or more devices (block  210 ) configured to perform one or more tasks (such as performing one or more medical procedures, including, but not limited to, a soft tissue luminal procedure, or the like), a device tracking system (block  215 ) configured to track each device, an anatomy and/or tool registration system (block  220 ) configured to register anatomy of a subject and/or tools used, anatomy or physiology (block  225 ) (i.e., anatomy or physiology of subjects or patients, or the like), one or more subjects or patients (block  230 ), a validation system (block  235 ) configured to validate information, a mapping or navigation system (block  240 ), one or more imaging systems (block  245 ), one or more sensors (block  250 ), data (particularly, regarding the one or more devices and/or the device tracking, or the like) (block  255 ), extended reality (“XR”) platform or hardware (block  260 ), user experience (“UX”) device or system (block  265 ), information display (block  270 ), or regulatory pathway system (block  275 ), and/or the like. The blocks  210  and  215  corresponding to the one or more devices and the device tracking system, respectively, are directed to or focused on the device or instrument aspects  280  of the system, while the blocks  225  and  230  corresponding to the anatomy or physiology and the one or more subjects, respectively, are directed to or focused on aspects of the subject(s) or patient(s)  285 . Likewise, the blocks  245 - 255  corresponding to the one or more imaging systems, the one or more sensors, and the data, respectively, are directed to or focused on the data collection aspects  290  of the system, while the blocks  260 - 270  corresponding to the XR hardware, the UX device or system, and the information display, respectively, are directed to or focused on the user interface aspects  295 . 
     According to some embodiments, the one or more devices (at block  210 ) may correspond to (or may include) the one or more devices or equipment  135  of system  100  of  FIG.  1   , or the like. 
     In some embodiments, the mapping or navigation system (at block  240 ) might include, without limitation, at least one of an XR computing system, a medical procedure computing system, a hub computing system, a three-dimensional (“3D”) graphical processing unit, a cluster computing system, a four-dimensional (“4D”) graphics computing system, a server computer, a cloud computing system, or a distributed computing system, and/or the like. 
     According to some embodiments, the one or more imaging systems (at block  245 ) may correspond to (or may include) the one or more imaging devices or systems  140  of system  100  of  FIG.  1   , or the like. 
     In some embodiments, the one or more sensors (at block  250 ) may correspond to (or may include) the one or more sensors  145  of system  100  of  FIG.  1   , or the like. 
     According to some embodiments, the user interface aspects  295  (at blocks  260 - 270 ) might include, but not limited to, at least one of a headset, UX glasses, a viewing window, a supplement to existing glasses, headphones, UX contact lenses, a heads-up display (“HUD”) device, a 3D spatial sound system, a telemonitoring system, a rigid robotic device control and sensory feedback system, a soft robotic device control and sensory feedback system, an eye control system, a voice control system, a remote control system, a gesture-based control system, a sign language-based control system, a body-part-based control system, a joystick, a mouse, a two-dimensional (“2D”) screen display, a 3D refractive display, a parallel reality system, a projection system, a 3D printed reconstruction system, a customized view generation system, a ghosting and prediction system, a master-slave control system, an annotation system, or a haptic feedback system, and/or the like. 
       FIG.  3    is a schematic diagram illustrating a non-limiting example  300  of a process stack for implementing an IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
     With reference to the non-limiting example  300  of  FIG.  3   , a general process stack for implementing an IA or EI ecosystem for a soft tissue luminal procedure, which may include, but is not limited to, at least one of an atrioventricular dual chamber sensing and pacing procedure, a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), an endoluminal procedure, a cardiac endoluminal procedure, a pulmonary endoluminal procedure, a gastrointestinal endoluminal procedure, a neurovascular endoluminal procedure, a peripheral vascular endoluminal procedure, a surgical procedure, a left atrial appendage (“LAA”) procedure, a tissue ablation procedure, a transcatheter aortic valve repair (“TAVr”) procedure, a transcatheter aortic valve replacement (“TAVR”) procedure, a transcatheter mitral valve repair (“TMVr”) procedure, a transcatheter mitral valve replacement (“TMVR”) procedure, a transcatheter pulmonic valve repair (“TPVr”) procedure, a transcatheter pulmonic valve replacement (“TPVR”) procedure, a transcatheter tricuspid valve repair (“TTVr”) procedure, a transcatheter tricuspid valve replacement (“TTVR”) procedure, a mitral clip repair procedure, a shunt procedure, a coronary angioplasty procedure, a balloon angioplasty, a stenting procedure, an atrial septal defect (“ASD”) treatment procedure, a cardiac shunt treatment procedure, a heart bypass procedure, a cardiac mapping procedure, a cardiac resynchronization therapy (“CRT”) device installation procedure, a catheter ablation procedure, an endovascular repair procedure, a heart monitor installation procedure, an implantable cardioverter defibrillator (“ICD”) device installation procedure, an extravascular ICD (“EV-ICD”) device installation procedure, a minimally invasive endovascular repair procedure, a miniature leadless implant installation procedure, a miniature leadless pacemaker installation procedure, an implantable sensor installation procedure, a surgical heart valve repair and replacement procedure, a transcatheter pulmonary valve (“TPV”) therapy, a ventricular assist device (“VAD”) installation procedure, an intra-aortic balloon pump (“IABP”) implantation procedure, a heart transplant operation, a cryoballoon or cryoablation catheter procedure, a pulsed field ablation (“PFA”) procedure, an electroporation procedure, a radio frequency (“RF”) ablation procedure, a phased RF (“pRF”) ablation procedure, a microwave (“MW”) ablation procedure, a laser ablation procedure, a radiation ablation procedure, a microwave ablation procedure, a high intensity focused ultrasound (“HIFU”) procedure, a histotripsy procedure, an abdominal aortic aneurysm (“AAA”) procedure, a thoracic aortic aneurysm (“TAA”) procedure, a thoracoabdominal aortic aneurysm (“TAAA”) procedure, a complex aortic arch aneurysm procedure, a vascular occlusion procedure, an atherectomy procedure, a renal denervation procedure, a deep vein thrombosis (“DVT”) procedure, a thrombectomy procedure, a flow diversion endoluminal procedure, or a neuro stenting procedure, and/or the like. Such a process stack may begin with a selection of a soft tissue luminal procedure or operation (e.g., a VFA procedure, or the like) (at block  305 ). In response to such selection, a soft tissue luminal procedure system (including, but not limited to, implantation of a miniature leadless implant, implantation of a pacer or pacemaker, or the like) may be implemented (at block  310 ). 
     The IA or EI ecosystem might utilize, in conjunction with the soft tissue luminal procedure system (with sensors on the therapy delivering device also being referred to as “contacting internal sensors” or the like), a number of devices or equipment, sensors, imaging systems, and other systems (collectively, “soft tissue luminal procedure sub-systems” or the like), including, but not limited to, at least one of an implant device (at block  315 ), a power generator (at block  320 ), a remote control and/or foot pedal control system (at block  325 ), or one or more patient sensors (at block  330 ), and/or the like. In some instances, the one or more patient sensors may include, but are not limited to, at least one of one or more room-based sensors (also referred to as “contactless sensors” or the like), an electrocardiogram (“ECG”) monitor, a mapping vest (e.g., CardioInsight™ (“CIT”) mapping vest, or the like; also referred to as “contacting external sensors” or the like), one or more other wearable sensors, one or more implanted sensors, and/or other patient sensors. The IA or EI ecosystem may further include, without limitation, a mapping and/or navigation system (at block  335 ), imaging modality (at block  340 ), a system hub and/or application programming interfaces (“API&#39;s”) (at block  345 ), cloud storage (at block  350 ), or artificial intelligence (“AI”) and/or data analytics (at block  355 ), a programmer system  360  (similar to programmer system  205   a  of  FIG.  2   , or the like), and/or the like. In some cases, the mapping vest CardioInsight™ (“CIT”) mapping vest, or the like, may be a single-use, disposable, multi-electrode vest that gathers cardiac electrophysiological data from the surface of the patient&#39;s body, with such data being combined with imaging data taken of the patient to produce and display simultaneous, bi-atrial and bi-ventricular 3D cardiac maps, or the like. Data from these sub-systems may be combined to generate a combined display of three-dimensional (“3D”) information or four-dimensional (“4D”) information (i.e., three-dimensional (“3D”) information plus at least one of real-time updates, dynamic modeling, or data streaming, and/or the like) for cognitive load (at block  365 ). Such 3D or 4D information may be used to control therapy delivery over the system (e.g., via use of robotic systems, robotic arms, handheld robotic systems, robotic systems integrated into a device handle, or the like) (at block  370 ). The process at blocks  310 - 360  may be repeated as necessary or as desired. 
     Table 1 below illustrates a specific non-limiting example combination(s) of sensors, imaging systems, tracking (or mapping and navigation) systems, and devices (e.g., robotic devices or the like) for each of the Pre-Operative Planning Stage, the Intra-Operative Adjustment Stage, and the Post-Operative Monitory Stage (as described below with respect to  FIG.  5 B ) while implementing an IA or EI ecosystem for a soft tissue luminal procedure. The various embodiments, however, are not limited to these items nor their specific combinations and can include any combination of items listed for sensors, imaging systems, tracking (or mapping and navigation) systems, and devices, e.g., as described below with respect to  FIG.  7   , or the like. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Combination List of Systems and Devices for implementing 
               
               
                 IA or EI ecosystem for a soft tissue luminal procedure. 
               
            
           
           
               
               
               
            
               
                   
                   
                 Post-Op 
               
               
                 Pre-Op 
                 Intra-Op 
                 Stage 
               
               
                 Stage 
                 Stage 
                 Monitoring/ 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Integrated 
                 Imaging 
                 Navigation/ 
                   
                 Integrated 
                 AI/Data 
                 Analytics 
               
               
                 Diagnostics 
                 Devices 
                 Mapping 
                 Sensor Devices 
                 Robotics 
                 Analytics 
                 Feedback 
               
               
                   
               
               
                 Biometric 
                 ICE US, 
                 Fluoro, 
                 Contactless 
                 ILR + AR 
                 Merging 
                 Output of 
               
               
                 Input, 
                 EWI 
                 ICE, EWI 
                 Optical, 
                 and 
                 Imaging 
                 Acute and 
               
               
                 Morphology 
                 US, 
                 merged 
                 IR Temp, 
                 Haptics 
                 and 
                 Chronic 
               
               
                 Inputs, 
                 Fluoro 
                 with EM, 
                 Blood Pressure, 
                 Feedback, 
                 Other 
                 Outcomes + 
               
               
                 CT or MRI 
                   
                 Impedance, 
                 Heart Rate, 
                 Eye 
                 Sensor 
                 Feedback 
               
               
                   
                   
                 or RFID on 
                 Motion from 
                 Tracking, 
                 Data 
                 Loop 
               
               
                   
                   
                 a 3D XR 
                 Phrenic Damage, 
                 Trajectory 
               
               
                   
                   
                 real time 
                 Respiratory Rate, 
                 Coordinate 
               
               
                   
                   
                 UI 
                 Motion of 
                 Tracking, 
               
               
                   
                   
                   
                 Fiducials, 
                 Impedance 
               
               
                   
                   
                   
                 Tool Object 
                 of Tip 
               
               
                   
                   
                   
                 Recognition, 
                 Electrode 
               
               
                   
                   
                   
                 Collision 
               
               
                   
                   
                   
                 Detection, Room 
               
               
                   
                   
                   
                 Traffic Flow, 
               
               
                   
                   
                   
                 Fatigue/Overload, 
               
               
                   
                   
                   
                 Nociception 
               
               
                   
                   
                   
                 Monitoring, Bed 
               
               
                   
                   
                   
                 Sensors, 
               
               
                   
                   
                   
                 Impedance of Tip 
               
               
                   
                   
                   
                 Electrode 
               
               
                   
               
            
           
         
       
     
     As shown in Table 1 above, for the soft tissue luminal procedure, the IA or EI ecosystem may utilize, for an integrated diagnostics system during the Pre-Operative Planning Stage, one or a combination of biometric input (including, but not limited to, information regarding at least one of age, weight, height, gender, race, etc.), morphology inputs (such as cardiac history including, without limitation, persistent versus paroxysmal, etc.), and/or a computed tomography (“CT”) system or a magnetic resonance imaging (“MRI”) system, and/or the like. In some cases, the IA or EI ecosystem may perform analysis of data obtained by these sensors, imaging systems, and/or tracking (or mapping and navigation) systems as well as user input to generate recommendations or to facilitate physician/user plans for the soft tissue luminal procedure. The soft tissue luminal procedure may then be performed based on sensor data, imaging data, tracking data, recommendations, and/or physician/user plans obtained or arising from the Pre-Operative Planning Stage. 
     During the Intra-Operative Adjustment Stage, the IA or EI ecosystem may combine (1) imaging device(s) including one or a combination of an intra-cardiac echocardiography (“ICE”) ultrasound (“US”) system, an electromechanical wave imaging (“EWI”) US system, and/or fluoroscopy system, with (2) navigation/mapping system(s) including one or a combination of fluoroscopy, ICE, EWI merged with an electromagnetic (“EM”) mapping and/or navigation system, impedance-based tracking system, or a radio frequency identification (“RFID”)-based tracking system on a 3D XR real-time user interface (“UI”), with (3) sensor device(s) including one or a combination of a contactless optical-based tracking system, an infrared (“IR”)-based tracking system for sensing temperature, a blood pressure sensor, a heart rate sensor, a motion sensor (e.g., to track motion from phrenic damage, or the like), a respiratory rate sensor, a fiducial tracking sensor to track motion of fiducials to maintain imaging/mapping/navigation alignment, a surgical tool object recognition system, robotic collision detection system, tracking system for tracking traffic flow in a room, user fatigue and/or cognitive overload detector, nociception monitoring sensors, patient headset sensors, bed or mattress sensors, and/or use of impedance of the tip electrode to detect perforation into the left ventricle (“LV”) as intra-operative sensors, and/or the like, with (4) integrated robotics including a soft-tissue intra-luminal robotic (“ILR”) system (where an intra-luminal device refers to a device that is configured to be introduced into a lumen in the body of a patient or subject, where a lumen refers to the cavity or channel within a tubular organ, such as a blood vessel (e.g., artery, vein, or capillary), esophagus, trachea, and intestine, etc.), eye tracking system for tracking physicians with XR headset relative to robotics for cognitive load and safety shut offs, trajectory coordinate tracking of physical catheters relative to digital twins for replay, approach angle, etc., to relate back to outcomes, and/or using the impedance of the tip electrode to detect perforation into the LV as intra-operative sensors and intra-operative analytics, with (5) intra-operative AI and/or data analytics (i.e., real-time or near-real-time predictive and prescriptive analytics, etc.) that merges imaging and other sensor data to adjust device configurations, settings, and/or implementations in real-time (or near-real-time (e.g., within milliseconds or seconds, etc.)) based on any updates or changes to the sensor data, imaging data, tracking data, and/or recommendations obtained during the Intra-Operative Adjustments Stage. The ILR system may combine an intra-luminal robotic device with AR assistance and haptics feedback, particularly, for navigating to locations in within the heart, and/or navigating to pulmonary vein isolation (“PVI”) plus (“PVI+”), with complex targets outside of the pulmonary vein. 
     In some embodiments, the merged imaging and other sensor data for the intra-operative AI and/or data analytics may include, without limitation, the following combinations: (a) spatial sensor data combined with delivery system or catheter itself and capital equipment time series data from sensors for temperature, voltage, current, and/or waveforms; (b) combination of delivery system or catheter and capital equipment data for electrical field proximity to targets with predictive analytics from image data merge and previous post-operative feedback loops; (c) auto-segmentation of anatomical imaging, object/feature recognition, trajectory recommendation to target and real-time tracking, 3D XR with catheter and implant electrode proximity to location or gap, electrical field, tagging of therapy, predictive rhythm change to titer therapy, proximity to adjacent anatomy (e.g., tricuspid valve, mitral valve, or atrioventricular node, etc.) with warnings to recommend approach and to titer therapy, and provide device and size recommendations; (d) XR headset eye tracking and robotic coordinates for safety, efficiency, and relationships with digital twins (e.g., “back-up camera” following moving anatomy that has real-time or near-real-time distance and auditory feedback until “docked”); (e) facial and/or text recognition to anonymize people and/or documents; or (f) impedance from the tip electrode to detect perforation into the LV; etc. 
     During the Post-Operative Monitory Stage, the IA or EI ecosystem may utilize the output of acute and chronic outcomes assessed per inputs from the Integrated Diagnostics, the Imaging Devices, Navigation/Mapping, and/or Sensor Devices. A feedback loop may be used to continually enhance predictive and prescriptive recommendations in real-time or near-real-time, with playback of trajectories and/or electrical conduction, descriptive analytics, suggested morphologies from similar patients, literature, etc. In this manner, the combinations during all three stages (i.e., the Pre-Operative Planning Stage, the Intra-Operative Adjustment Stage, and the Post-Operative Monitory Stage) may achieve personalized trajectories and titered therapy for long term efficacy with minimal safety risk, while providing the user with efficient application of the therapy (i.e., reduced or consistent time, and increased throughput) and extended career longevity (with no or low fluoro). 
       FIG.  4    is a flow diagram illustrating a method for implementing an IA or EI ecosystem, in accordance with various embodiments. 
     In the non-limiting embodiment of  FIG.  4   , method  400  might comprise, at block  405 , acquiring pre-procedural work-up, in some cases, including, but not limited to, ultrasound, computed tomography (“CT”), magnetic resonance imaging (“MRI”), electrocardiogram (“ECG”), lifestyle, biometrics, or respiratory waveforms, and/or the like. At block  410 , method  400  might comprise querying a pre-procedural artificial intelligence (“AI”) cloud database for disease trigger data, including, but not limited to, data regarding anatomical morphology prognosis. Method  400 , at block  415 , might comprise analyzing the trigger data from pre-procedural AI cloud database, including, without limitation, feature recognition of likely targets. 
     Method  400  might further comprise, at block  420 , recommending, predicting, or prescribing at least one of device type, size, procedural steps, target locations, trajectories, or statistical success rates of similar patients and procedures, and/or the like. In some cases, such recommendations might include, without limitation, introducer sheath (e.g.,  4 F sized introducer sheath, or the like), capsule size, capsule orientation, proximity to target, introducer sheath (e.g.,  12 F sized introducer sheath, or the like), settings or configurations for the delivery system (e.g., VFA delivery system or the like), compliance, or interface to contact, and/or the like. 
     At block  425 , method  400  might comprise communicating the analyses and/or recommendations via, e.g., unity hub or similar system to cloud or edge data system, equipment, capital equipment, etc. Herein, 3D engine refers a platform (e.g., Unity© engine hub or Unreal Engine© hub, or the like) for creating and operating interactive, real-time 3D (“RT3D” or “4D”) content that integrates 3D models for a “scene” and software code (e.g., C#, C++, etc.) that breathes life into features including, but not limited to, coloration, animation, user interfaces, collision detection, and/or other physics-based elements (e.g., trajectory paths, cardiac electrical wave propagation, blood flow/pressure/velocity, etc.). The unity hub refers to a standalone application that streamlines searching, downloading, and managing 3D engine projects and installations. The unity hub is the integration point of the real-world devices with the AR industry, but can add data-related features to enhance the UI via AI or other algorithms, or the like. Herein, capital equipment refers to equipment that is associated with therapy delivery (e.g., mapping and navigation equipment, robotics systems, ablation generators, etc.). In some cases, the capital equipment may provide information to/from the XR unity hub. Method  400  might further comprise displaying data and models on capital equipment, in some cases, projecting slaved imaged on at least one of one or more operating room (“OR”) screens, one or more tablets, one or more extended reality (“XR”) headsets, or one or more XR spatial projections, and/or the like (block  430 ). In some cases, the capital equipment might include, without limitation, delivery systems, robotics systems, or imaging modality and navigation, and/or the like. 
     The processes at blocks  405 - 430  might correspond to pre-procedural processes. 
     At block  435 , method  400  might comprise acquiring system data, patient sensor data, and imaging data, one or more of which might be real-time or near-real-time data (where “near-real-time” herein refers to within milliseconds or seconds, or the like). For example, auto-segmentation of an anatomy is beneficial in minutes compared to manual segmentation but may need to be an order of magnitude better (i.e., faster) to drive real change—hence, “near-real-time” or millisecond or second data acquisition times. In some embodiments, the system data and patient sensor data might include, without limitation, optical data, doppler flow data, pressure data, respiration data, ECG data, neurological event data, navigation coordinate data, and/or the like. According to some embodiments, the imaging data might include, but is not limited to, electromechanical wave imaging (“EWI”) data and/or ultrasound data, or intra-cardiac echocardiography (“ICE”) data, and/or the like. In some instances, as part of the navigation process for the device (e.g., miniature leadless implant, pacemaker, etc.), an echogenic delivery system may be used, In some embodiments, programming the device (e.g., miniature leadless implant, pacemaker, etc.), in some cases using a programmer system (e.g., programmer system  205   a , or the like), may create a link between the programmer system and other components of the IA or EI ecosystem, with all the data from the device and the programmer system being integrated within the remainder of the data (e.g., data from impedance checks, ECG morphology, etc.) to confirm capture of the conduction system, etc. 
     Method  400 , at block  440 , might comprise communicating unity hub system application programming interfaces (“API&#39;s”) for cloud and edge devices, in some cases, as the acquired data are merged. Method  400  might further comprise generating or co-registering virtual and actual fiducials, in some cases, via at least one of digital imaging and communications in medicine (“DICOM”) files, headset mapping and/or photogrammetry, skin patches, reflective infrared markers, impedance, electromagnetic, radio frequency identifiers (“RFIDs”), or Bluetooth™-based devices, and/or the like (block  445 ). Herein, the term “fiducials” may refer to an object that is placed in the field of view of an imaging system that appears in the produced image as a point of reference or a measure, and/or to landmarks (whether anatomical or other) that are employed to co-register the digital world with the real world. 
     Method  400  might further comprise, at block  450 , generating or calibrating virtual to real (or physical or non-digital) device and capital, target anatomy, adjacent structures, tissue selectivity, safety shut-offs (e.g., no delivery without focused eye gaze), etc. At block  455 , method  400  might comprise analyzing merged or analyzed data, predictions, prescriptions, real-time flow changes and/or pressure changes, and/or the like, as it relates to device location and therapy delivery. Method  400 , at block  460 , might comprise displaying combined data, imaging, and predictions in enhanced state, in some cases or preferably, via XR spatial projections or headset (in some cases, via computational fluid dynamics, finite element analysis, etc.). Herein, enhanced state refers to the state in which information that is presented to the user (such as the combined data, imaging, and predictions, or the like, as preferably displayed via the XR spatial projections or headset) exceed what would normally be perceived by the unaided human eye alone. In some cases, the enhanced state may refer to enhancements to the unaided human senses in addition to visual enhancements as described above, including, but not limited to, auditory enhancements (e.g., surround sound, voice commands, auditory information presentation, etc.), haptic-like feedback (e.g., haptic feedback for robotic or instrument control when interacting with objects or tissue, etc.), or the like. 
     Method  400  might further comprise controlling device, robotics, capital equipment settings, therapy delivery through system interaction, including, but not limited to, voice commands, eye gaze, virtual buttons, virtual triggers, electrocardiogram (“ECG”) gating, AI algorithm, etc., by one or more team members within the operating room or remotely (block  465 ). Method  400  might further comprise, at block  470 , analyzing data changes—including, without limitation, at least one of pressure, flow, EAM changes via CIT vest and/or US/EWI, transient ischemic attack (“TIA”), biometric data, and/or the like, relative to database. In some instances, the data changes might be real-time or near-real-time data. 
     Based on the analysis (at block  470 ), method  400  either might continue onto the process at block  475  or might return to the process at block  435 . The processes at blocks  435 - 470  might correspond to intra-procedural processes. 
     At block  475 , method  400  might comprise communicating aggregate time series data via the hub system to cloud or edge data system, equipment, capital equipment, etc. Method  400 , at block  480 , might comprise analyzing the aggregated analytics for each part of the system relative to database. Method  400  might further comprise, at block  485 , displaying acute results relative to database, predicted long term success, three-dimensional (“3D”) replays, four-dimensional (“4D”) replays (i.e., 3D replays plus at least one of real-time updates, dynamic modeling, or data streaming, and/or the like), statistical dashboards, etc., in paper, two-dimensional (“2D”) digital, interactive 3D digital, and/or extended reality (“XR”) format, or the like. Method  400  might further comprise recommending ideal post-procedural care and follow-ups for value-based healthcare (block  490 ). Herein, value-based healthcare refers to a framework for restructuring healthcare systems with the principal goals of health outcomes per unit costs (i.e., value) for patients. At block  495 , method  400  might comprise communicating, de-identifying, and sending back to the cloud or edge data system for analyses and model improvement. Method  400  might return to the process at block  410 . The processes at blocks  475 - 495  might correspond to post-procedural processes. 
       FIG.  5 A  is a flow diagram illustrating a non-limiting example  500  of feedbacked interactions among three sub-layers of imbedded and contactless sensors, data, and vision systems that may be used as part of an IA or EI ecosystem implementation for soft tissue luminal applications, in accordance with various embodiments.  FIG.  5 B  is a flow diagram illustrating a non-limiting example  500 ′ of interactions among “pre-operative planning,” “intra-operative adjustments,” and “post-operative monitoring” with an optimization feedback loop, in accordance with various embodiments. 
     With reference to the non-limiting example  500  of  FIG.  5 A , the IA or EI ecosystem for implementing soft tissue luminal applications may employ three sub-layers of imbedded and contactless sensors, data, and vision systems working together in a feedback loop, the three sub-layers including, without limitation: (1) Top Sub-Layer (or Planning Layer)  505 , (2) Intermediate Sub-Layer (or Decision Layer)  510 , and (3) Base Sub-Layer (or Action Layer or Execution Layer)  515 . These various sub-layers feedback on each other (as depicted in  FIG.  5 A  by the arrows connecting one sub-layer to another and back again). 
     According to some embodiments, the Top Sub-Layer (Planning Layer)  505  represents a high-level layer that captures the entire room including large movements of the patient, medical staff, and medical equipment such as the robotic linkages and delivery system. This can be done by multiple techniques together or separately, including, but not limited to, infrared and photogrammetry techniques. For example, one or more depth cameras (e.g., Kinect™ depth cameras, or the like) may be used to map the entire room, to track distances between users, to track distances between robotic linkages, and to track position(s), orientations(s), and/or movement(s) of the patient. Algorithms with known degrees of freedom of each of these can determine the range of motion, and machine learning simulations can recommend a location(s) of each in real-time or near-real-time. This can then be displayed on the Intermediate Layer  510  for the user(s) or medical professional(s) to act via the Base Layer  515 . 
     Information and/or outcomes facilitated by the Top Sub-Layer  505  may include, without limitation, (a) equipment and/or robotic system setup monitoring and suggestions to the next layer (which may aid in robot and human collision predictions and warnings, enabling optimized robot set-ups for patient-specific target anatomy and maximum workspace, and which may utilize machine learning (“ML”) that takes into account factors (e.g., body morphology, type of procedure, room measurement, etc.) to recommend optimal placement of equipment/devices (e.g., surgical robotics arms, actuators, etc.) or the like); (b) staff location and workflow monitoring (including ensuring social distancing protocols are met, producing performance metrics of the procedure (e.g., time taken for each sub-task in the procedure, those involved, the order of the tasks, etc.), linking performance metrics to a range of outcomes and providing feedback to the medical professionals, or the like); (c) obtaining or generating port (or surgical device placement) locations, setup, monitoring, and/or suggestions, or the like; (d) monitoring patient posture, respiration, shifting positions, or the like; (e) monitoring biometrics of patient and/or staff (including, but not limited to, temperature, heart rate, oxygen level, respiratory rate, body mass index (“BMI”), age, gender, height, weight, ethnicity, etc.); (f) monitoring staff safety via vision tracking or the like (in some cases, via facial recognition AI techniques) to ensure safety protocols are met (including, but not limited to, proper face mask wearing, sterile fields, identification of all equipment and disposables, etc.); (g) tracking cell operating system (“COS”) layouts and traffic flow (i.e., using spatial mapping, or the like); (h) producing workflows for equipment and staff (in some cases, displayed in heat maps and pathways, or the like); (i) automating documentation while blurring out or redacting private areas of text and facial features, or the like; and/or the like. 
     In some instances, modular design of the robotics system or platform (e.g., the Hugo robotics platform, or the like) may enable integration across broad therapeutic areas, including endoluminal procedures, or the like. The surgeon console and visualization tower may be used for endoluminal cases, paired with a modified arm cart with an adapter for steerable guide catheters. Other arm carts may be equipped with companion therapeutic and diagnostic tools. Robotic solutions may include, without limitation, at least one of motion compensation, force feedback, force control, image guidance, or endoluminal catheter delivery, and/or the like. Regarding motion compensation, the robot may automatically control the end effector to synchronize with cyclic motion due to heart or respiratory motion. The robot may detect the cyclic motion and may respond accordingly to synchronize its motion to the motions of the tissue structures. The cyclic motion can also be estimated using Kalman filtering techniques or using machine learning approaches by integrating post-op sensor data, or the like. Regarding force feedback, the manipulator (i.e., end effector and/or catheter body) may sense contact forces (in multiple degrees-of-freedom) with the environment (e.g., tissue structures) and may provide feedback to the surgeon via haptics, AR visual cues, auditory tones, or other means, etc. Regarding force control, the robot software controls may be used to regulate forces applied to the environment by the robot (e.g., maintaining exactly 1 N of force on the endoluminal surface by a guide catheter, etc.). Regarding image guidance, imaging systems (e.g., ultrasound probe on a catheter with display integrated with the surgeon console and tower) in conjunction with computer vision algorithms may be used to automatically guide the robot&#39;s motion and/or trajectory in real-time. These algorithms may be enhanced by combining with pre-op image scans and post-op robot kinematic data. Variations of ultrasound imaging such as EWI and Photoacoustic Imaging may further enhance such integration. Regarding endoluminal catheter delivery, combining device positional tracking (e.g., EM) and anatomical imaging (e.g., MRI, CT, fluoroscopy, ultrasound, etc.), a modified cart arm may automatically advance guide catheters, access tools, and therapy delivery tools into the patient anatomy particularly endoluminally (i.e., within luminal portions of the body), potentially eliminating need of a bronchoscope, delivery system, catheter, or probe, which limits device size and performance due to working channel diameter and length. 
     In some embodiments, the Intermediate Sub-Layer (Decision Layer)  510  represents decision-making tools that provide the interface and feedback loop(s) between the user(s) or medical professional(s), the equipment, and the devices. For example, this layer may be used to gather non-contact sensor data from the Top Sub-Layer  505  and tool and/or patient data from the Base Sub-Layer  515 , then to analyze, recommend, and display for the user(s) or medical professional(s) to make an Extended Intelligent (“EI”) decision(s). These recommendations can come with displayed boundaries and/or guides, probability of success, recommended similar cases/approaches/outcomes for reference, etc. This will allow the user(s) or medical professional(s) to interact with a virtual mockup of the system, to move around components of the system, to compare options to optimize, and/or to verify placement once moved, and/or the like. 
     Features of the Intermediate Sub-Layer may include, without limitation, (a) visualizing and integrating 3D anatomical structures in near-real-time (i.e., in 4D) at this level in many ways, including, but not limited to, via a mapping vest (e.g., an electrocardiogram (“ECG”) sensor belt or CardioInsight™ (“CIT”) mapping vest, or the like), magnetic resonance imaging (“MRI”), or via electromechanical wave imaging (“EWI”) ultrasound, or the like; (b) enabling equipment setup, including virtual overlays of base and articulating sections (e.g., using fluorescing molecule visualization integration to inject fluorescing agents that selectively bind to targeted structures thus enhancing their visualization, or providing an indication of dynamic perfusion across the field of view of a camera system combined with use of ICG dye for blood flow, etc.); (c) providing remote assistance, tele-mentoring, and tele-surgery via display (where the display and control may be through multiple modalities, including, but not limited to, headsets, laptops, phones, tablets, command center, etc.); (d) utilizing imbedded sensors—including, but not limited to, microelectromechanical system (“MEMS”)-based sensors, vibration sensors, rotation sensors, acceleration sensors, temperature sensors, etc.—to provide immediate feedback loop(s) for highlighting and/or addressing concerns such as robotic arm and/or human collisions, or for otherwise aiding in validating non-contact sensing, or the like; (e) integrating XR analytics data sources and machine learning including security and privacy (e.g., blurring out faces of patients, physicians, staff, etc., using facial recognition techniques; blurring out or redacting personal, patient or healthcare-related data, etc., in written documents using object recognition techniques; and/or the like); and/or the like. In some cases, machine learning methods for visual tracking, human pose estimation, human-object interaction models, tool tracking, estimate durations of activities, suggested tool types, procedure techniques, recovery planning, and change-over for next procedure(s) may include, but are not limited to, (i) predictive movements from Convolutional Neural Network and Fully Connected Networks, or the like; (ii) Sequential decision-making such as Markov Decision Process (“MDP”), or the like; and/or (iii) AI or machine learning inputs (including, without limitation, at least one of survival analysis models that combine machine learning with survival function estimation that estimates a specific survival curve for each patient, representing each patient&#39;s probability of risk for a particular disorder over time, etc.). 
     For the survival analysis models, features may be inputted into a machine learning model that outputs a risk score. This risk score may be used to rank patients by how likely the model determines that each patient will develop recurrent disorder (e.g., atrial fibrillation (“AF”), bradycardia, or other arrhythmia, or the like). The higher a patient&#39;s risk score, the more likely the patient will experience the disorder in question, according to the model. Next, the risk score may be entered into the semi-parametric survival model. In some cases, a Breslow estimator may be used to estimate the baseline survival function, S 0 (t). Thus, each patient with a different risk score will have a unique personal survival curve, S(t). 
     One benefit of using survival analysis is that censored data may be handled very effectively. Using the case of recurrent AF, for example, censored data refers to following of a patient being stopped before they are observed to have recurrent AF. It is difficult to use classical regression in such cases because it is unknown exactly when these patients experience recurrent AF. Survival analysis offers an elegant solution. For each patient, two variables are needed to represent the outcome: (i) whether a patient was observed to have recurrent AF; and (ii) the duration before the patient either had recurrent AF or stopped being followed. One assumption of semi-parametric survival models, however, is that they assume proportional hazards. This means that the ratio of any two patients&#39; hazards (i.e., slope of the survival curve) is constant over time. Thus, the shape of all patients&#39; predicted survival curves is the same, just scaled by a different risk score. 
     The Cox proportional hazards model (“CPH”), random survival forest (“RSF”), and gradient boosted survival model (“GBM”) are three types of semi-parametric models. The CPH is a very basic model that uses linear regression as the machine learning model or algorithm that outputs the risk score. The RSF and GBM are more complex than the CPH. They can find non-linear trends, and are considered state-of-the-art. The RSF implements a random forest as the machine learning model, while the GBM implements gradient boosted trees as the machine learning model. The RSF is more robust to noise in the data and is trained more quickly. Hyperparameter tuning also does not affect performance as much. The GBM, other the other hand, is more sensitive to noise in the data and is trained more slowly. Hyperparameter tuning affects the performance of the GBM more than in the RSF. It has been shown that GBMs can achieve better performance than RSFs. 
     To evaluate the performance of a survival model, a concordance index may be used. The concordance index is a ranking metric. For each pair of patients, they are said to be concordant if the one with the higher risk score predicted by the model is also the one that experience recurrent AF first. The concordance index is the fraction of all patient pairs that are concordant. Logically, the concordance index ranges from 0 (the worst) to 1 (the best). A dummy model, which guesses randomly, would achieve a concordance index of 0.5. 
     There are several directions that a virtuous feedback loop could follow in clinical practice. Due to the different underlying signals in procedure datasets, it would be exceedingly difficult to build a single survival analysis model that could be applied to all ablation therapies everywhere. Instead, a more practical solution would be to create customized survival analysis models for individual clinics, individual hospitals, or other entities where the ablation therapy procedure can be expected to be performed uniformly and consistently each time. This way, the signal that may be modelled would be constant. It would be ideal to create separate models for patients with regular pulmonary vein (“PV”) anatomy and patients with each type of irregular PV anatomy. This requires a very large patient population from a single entity to build the model, which is a major constraint to consider. 
     Another direction is to use the model to predict—before surgery—which patients would respond best to cryotherapy, or to VFA with bradycardia, or the like. In other words, the model would predict which patients would be the best candidates for cryotherapy. One approach is to create a model that could be used immediately after surgery to predict how effective or well-done the actual cryotherapy procedure was. However, since almost all the most important features were patient characteristics that could be determined before surgery and very few were procedure-related features, the model would be better suited to predict which patients would be best suited for cryotherapy before surgery. 
     In some embodiments, different sources of data may also be modelled. Although the modeling and discussion above has been centered on features where each patient has one value for each feature, more complicated types of patient data such as time series data and even imaging data may be considered. For example, more complex models may be built that analyze and predict risk of AF recurrence using the time series temperature data from cryoablation procedures. Further, image processing models may be incorporated that work on electro-anatomical maps, CT reconstructions, and other mapping and navigational image data. In some cases, relationships and connections between image data collected during cryotherapy procedure and risk of AF recurrence may be analyzed. 
     For the model, the following information may be used: (a) patient information (e.g., gender, race, height, weight, BMI, heart rate, systolic/diastolic blood pressure, smoking history, alcohol consumption history, number of years in paroxysmal AF, left atrium diameter, CHA 2 DS 2 -VASc score (reflective of congestive heart failure, hypertension, age of 75 years or older, diabetes mellitus, stroke or transient ischemic attack (“TIA”), vascular disease, age 65-74 years, sex category), New York Heart Association (“NYHA”) functional classification, etc.); (b) relevant medical history (e.g., hypertension, myocardial infarction, dyslipidemia, diabetes, coronary artery disease, prior direct current (“dc”) cardioversion (which is a procedure that uses a defibrillator to deliver a controlled electric shock to the patient&#39;s heart in attempts to return heart rhythm to normal), etc.); (c) procedure information (i.e., time series &amp; anatomical); and (d) outcome information (e.g., whether or not patient was observed to experience recurrent AF, duration until recurrent AF episode or until patient stopped being tracked, etc.); etc. 
     According to some embodiments, for VFA applications, multiple image modalities may be combined with navigation and data, then further enhanced with advanced AI algorithms and real 3D XR visualization feedback loops. For example, to mark targets and adjacent structures (e.g., the coronary sinus (“CS”)), visualization fluorescing molecule visualization integration may be employed. In some instances, systemic injection of fluorescing agents may be used to selectively bind to targeted structures enhancing their visualization, which may be fed into the Intermediate Sub-Layer (or Decision Layer). For example, injected molecules could target the phrenic or the vagus nerve and may be energized with near-IR (“NIR”) light source on a delivery system or catheter. The fluorescing emission from nerves surrounding the anatomy could then be detected with a camera coupled with a NIR notch filter residing on the delivery system or catheter. The NIR video may be processed to add nerve visualization overlays onto a visible light video feed from the delivery system or catheter. With clear visualization of nerve location, damage to adjacent structures may be avoided or targeted in neuro-therapies (i.e., critical structure avoidance, etc.). 
     A key clinical challenge that physicians face in catheter guidance and pacemaker device implantation is that of identifying critical anatomical structures, both those who guide the surgeon to correct implant placement, as well as those the surgeon should avoid as best possible as their damage may result in significant morbidity to the patient and therapeutic complexity. Cardiac valve structures and sinuses as key examples. While visibility of such structures via fluoroscopy, the central imaging modality used in pacemaker implant placement, is limited, such structures may have a clearer imaging representation in supporting modalities such as ultrasound or pre-procedural electrocardiography (“ECG”)-gated computed tomography (“CT”), for instance. Automated detection and segmentation of these representations, utilizing novel machine learning and deep learning segmentation methodologies, can be used to highlight such critical structures to the physician, as well as to alert the physician to their proximity in both pre-procedural planning and intraoperative execution. Novel 2D-3D multi-modal non-rigid co-registration methodologies may then be used to co-register the AI-detected critical structures based on such helper modalities directly over the fluoroscopic visualization, enabling intraoperative alerts on critical structure proximity. 
     According to some embodiments, the Base Sub-Layer (Action Layer)  515  represents medical tools interfacing with the patient to apply therapy. The three sub-layers may have overlapping functions and can calibrate and/or validate each other throughout the procedure. 
     Some features of the Base Sub-Layer may include, without limitation, (a) providing navigation and mapping of surgical devices, using optical-based, RFID-based, electromagnetic-based, impedance-based, Bluetooth™-based, RF-based, and/or ultrasound-based navigation and mapping functionalities; (b) enabling a shared session across multiple locations that may be employed by multiple devices and/or platforms; (c) enabling virtual and/or augmented reality display at procedure location or at a separate location for tele-mentoring and/or tele-surgical control (i.e., where interaction with the virtual display can physically control the surgical device(s)), and, in some cases, utilizing haptic feedback within the control center, device handle, and/or gloves of the user(s) or medical professional(s) to achieve more realistic feedback loop(s) and optimal outcome(s); (d) utilizing multiple modalities for device tracking, with near-real-time recommendations of therapy target being displayed in augmented reality via feedback loop of the system, where the feedback loop may be used to predict and track catheter trajectories, angles, distance to target, etc.; (e) using nociception monitoring sensors for enabling integrated closed-loop systems within the EI ecosystem for administration of analgesics, identification of patients with higher likelihood for severe post-operative pain, determination of levels of nociception as the procedure takes place, informing medical professionals that additional analgesics may be required (e.g., during incision or ablation tasks, etc.) and/or sending a signal to an analgesic pump to automatically titrate more drugs into the patient during the procedure. In some instances, other features that may be displayed for better decision-making may include, but are not limited to, displaying in 3D both predicted trajectories (whether based on predictions via AI or based on annotations of the user&#39;s or medical professional&#39;s own pathway) and tracked (or actual) trajectory, and/or the like. In some instances, the various devices may be used to monitor phrenic nerve stimulation, which may be useful feedback during implantation of leaded devices or may be useful in programming pacing vectors for a CRT system with a quadrupole lead. Thus, the EI ecosystem may automatically choose the best vector by tracking pacing thresholds and using nerve monitoring in its algorithm. In some cases, the EI ecosystem may utilize yet another layer and feedback loop in the form of an XR integration with the patient (e.g., enabling the patient to wear an XR headset during a procedure to reduce pain (instead of or via use of less medication) and/or to communicate with and/or see what the medical professional is doing (such as in the case of difficult child birth or brain surgery where the headset sensors can be implemented (for eye tracking, speech recognition, etc.). 
     Referring to the non-limiting example  500 ′ of  FIG.  5 B , the IA or EI ecosystem for implementing soft tissue luminal applications may additionally employ the following three stages: (1) a Pre-Operative Planning Stage  520 ; (2) an Intra-Operative Adjustments Stage  525 ; and (3) a Post-Operative Monitoring and Optimization Feedback Stage  530 . The process may start at the Pre-Operative Planning Stage  520 , then proceed to the Intra-Operative Adjustments Stage  525 , subsequently proceed to the Post-Operative Monitoring and Optimization Feedback Stage  530 , and loop back (as necessary) to the Pre-Operative Planning Stage  520 , with the cycle looping as many times as required or as desired to ensure continued optimization of the soft tissue luminal applications. 
     At the Pre-Operative Planning Stage  520 , the IA or EI ecosystem may collect sensor data from one or more sensors (e.g., imaging system(s)  140 ,  245 , and  340 , sensor(s)  145 ,  250 , and  330 - 340 , etc. of  FIGS.  1 - 3   , or the like, which are discussed in detail above). In some cases, the IA or EI ecosystem may perform at least one of the functions of the Top Sub-Layer (Planning Layer)  505  as well as at least one of the functions of the Intermediate Sub-Layer (Decision Layer)  510 , as discussed above with respect to  FIG.  5 A . The soft tissue luminal procedure, as discussed herein, may then be performed based on sensor data, recommendations, and physician plans obtained or arising from the Pre-Operative Planning Stage  520 . 
     During the procedure itself, at the Intra-Operative Adjustments Stage  525 , the IA and EI ecosystem may continue to collect sensor data from the one or more sensors and may perform at least one of the functions of the Base Sub-Layer (Action or Execution Layer)  515  as well as at least one of the functions of the Intermediate Sub-Layer (Decision Layer)  510 , as discussed above with respect to  FIG.  5 A . Adjustments to the delivery of the CIED placement may be implemented in real-time based any updates or changes to the sensor data and recommendations obtained during the Intra-Operative Adjustments Stage  525 . 
     Following a predetermined time after the soft tissue luminal procedure (e.g., 30 days, 60 days, and/or 90 days, or the like), the Post-Operative Monitoring and Optimization Feedback Stage  530  may be performed. At the Post-Operative Monitoring and Optimization Feedback Stage  530 , the IA and EI ecosystem may collect sensor data from the one or more sensors and may once again perform the at least one of the functions of the Top Sub-Layer (Planning Layer)  505  as well as the at least one of the functions of the Intermediate Sub-Layer (Decision Layer)  510 , as discussed above with respect to  FIG.  5 A . In some cases, the IA and EI ecosystem may perform a different set or combination of functions of each of the Top Sub-Layer (Planning Layer)  505  and/or the Intermediate Sub-Layer (Decision Layer)  510 . The IA and EI ecosystem may determine based on the sensor data and recommendations obtained or arising from the Post-Operative Monitoring and Optimization Feedback Stage  530  whether there has been a change or a difference in the sensor data, and, if so, whether the change or difference is indicative of a positive change (e.g., an expected, successful positioning of a device toward one or more targeted locations within the soft tissue luminal portions (e.g., heart, lung, or other lumen) to perform a soft tissue luminal procedure, resulting in good results being obtained from the procedure, without adverse signs of stress or injury in the patient&#39;s body, or the like) or a negative change (e.g., where an issue arises from one or more of improper positioning of the device within the soft tissue luminal portions, results not being properly obtained from procedure, and/or adverse signs of stress or injury in the patient&#39;s body are observed, or the like). The operations of the Post-Operative Monitoring and Optimization Feedback Stage  530  may be performed repeatedly over a predetermined period (e.g., every day for a week, or the like), which may also be repeated the following one or more months. Based on the sensor data results and recommendations obtained during the Post-Operative Monitoring and Optimization Feedback Stage  530 , the process may loop back to the Pre-Operative Planning Stage  520 , the Intra-Operative Adjustments Stage  525 , and the Post-Operative Monitoring and Optimization Feedback Stage  530  during a follow-on soft tissue luminal procedure (or correction procedure), or the like. 
     In various embodiments, a miniaturized leadless pacemaker (e.g., a miniature leadless device described herein, or the like) may be implanted in one of the right atrium, the right ventricle, the coronary sinus vein, or the atrioventricular septal wall. In the case of a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”), the miniaturized leadless pacemaker may be implanted on the atrioventricular septal wall (as referred to as the “triangle of Koch”). These miniaturized leadless pacemakers may be implanted via soft tissue luminal approaches.  FIGS.  6 A- 6 N  (collectively, “ FIG.  6   ”) illustrate various non-limiting examples  600  of one or more surgical devices and techniques for implementing a leadless ventricle from atrium sensing and pacing system procedure (“VFA procedure”) for controlling positioning of the surgical device within the heart that may be part of implementation of IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. 
     VFA is a procedure to treat bradycardia by accessing either the jugular or femoral veins and implanting a leadless pacemaker in the right atrium at the Triangle of Koch (as shown, e.g., in  FIGS.  6 A and  6 B ). The tricuspid valve (“TV”) is more apical than the mitral valve, resulting in an atrioventricular septum (“AVS”) that separates the right atrium (“RA”) and the left ventricle (“LV”) (as depicted, e.g., in  FIGS.  6 A and  6 B ). The challenge exists in terms of both structural and electrical constraints with respect to a large rigid device being positioned within a relatively small volume, the location of which is a moving target. Additionally, there are several adjacent structures and thus physiologically parameters that need to be monitored simultaneously, thus the need for the AI or EI ecosystem to streamline the data and the process for the physician (e.g., reduce cognitive overload). The device (e.g., a VFA leadless pacemaker device, or the like) is shown in  FIGS.  3 C and  3 D . In some embodiments, the device (e.g., the VFA leadless pacemaker device, etc.) may comprise an anode, an atrial electrode, and a ventricular electrode (as depicted in  FIG.  6 C ), and may be deployed or delivered using a delivery system (as depicted, e.g., in  FIG.  6 D ). 
       FIG.  6 E  depicts an apparatus (referred to herein as a “SLED” or a “CRADLE,” or the like) that may be used as a patient or table-based support and actuation apparatus that receives a sterilized single use device (e.g., a delivery system or catheter for delivering a miniature leadless implant device, a pacer or pacemaker, or another device, etc.). In some cases, the apparatus may be a semi-autonomous work-step support. Several options may include, e.g., linking the therapy and the patient specific anatomy/conditions to a network of data that contains historic procedural data that can be mined by AI algorithms to provide suggestions to the physician on the best-outcome device use and placement in the patient. The use of a database and AI systems may be injected at any point in the therapy procedure to suggest the lowest-risk, best possible outcome scenario at every work-step during the application of the therapy. 
     The therapy delivery process may be either manually actuated through the console system or semi-autonomously supported to facilitate the loading operation. In some embodiments, the SLED apparatus may be replaced by other methods of interfacing with a therapy delivery system, such methods including, but not limited to a mobile robotic end-effector to enable integration with other computer-based platforms to enable scaling and/or leveraging economies by using on platform for a multitude of cross-discipline applications (e.g., general open surgery, orthopedic procedures, orthoscopic/minimally invasive surgeries, and the like). Alternatively, the SLED apparatus may be replaced by a handheld robotics system or a robotics system integrated into a device handle of an instrument operated by the physician, or the like. 
     For simplicity, the preceding description did not disclose the use of internal sensors was not disclosed in detail. Additional sensors may provide essential feedback to the control system to ensure safety bounds are met at each step of the therapy or to detect specific conditions within the body that may be used by the control system (e.g., hub or computing system, etc.) to inform the operator (e.g., physician, etc.) or for feedback when autonomous algorithms are employed to enhance control. Potential sensors may be used to detect physical conditions of the therapy system, such as force, position or distance, rate (e.g., linear or rotational speed), acceleration, temperature, thermal flow, fluid flow, and/or local orientation or position relative to gravity, or the like. Additionally, physiological sensors may be employed to detect electrical signals, impedance, chemical conditions or states, or biological conditions or states. 
     Regarding data collection and telemetry, the system (including the delivery catheter and integrated sensors, etc.) may inherently or automatically process data during the therapeutic procedure. This data may be stored throughout the procedure and may be linked to the patient&#39;s anatomical conditions via the image processing segment of the system architecture. This data may be stored for future retrieval or may be transmitted locally to receivers connected to local networks and other means of data storage, processing, and further transmission. The data collected and processed may be transmitted over the wired connection to the system console or may be transmitted over wireless connection (e.g., radio communication (telemetry), etc.) to the system console. 
     For the endovascular therapies described herein, a similar process of automation may be incorporated to simplify the therapy process and to enable optimal, consistent outcomes when administered by a wide range of operator skill/experience levels. 
     Using big data, the ecosystem may aid the physician by using historical implant data to predict likelihood of migration of the graft (or implant, etc.) if the user positions the graft in the targeted location. Factors such as anatomical motion and remodeling may be incorporated into the success prediction. In some embodiments, the user may virtually implant the graft or grafts into the 3D anatomy using XR and may use the AI to further optimize the positioning of the graft/grafts. Virtual landmarks (e.g., digital twins, etc.) may be superimposed onto the 3D anatomical image to aid location of the grafts for the user. If deviations occur during placement, the ecosystem may correct for this for any subsequent graft if there is a need for one. This would enable the user to make the best decision for the patient to be treated and to maximize long term success. 
       FIG.  6 F  depicts a workflow for preparing landmarks and visualization, prior to implantation of the device (e.g., VFA miniature leadless device (e.g., implant or pacemaker, etc.)) via one of the jugular vein(s) or the right femoral vein.  FIG.  6 G  depicts an example of general features to enable the EI ecosystem for a VFA procedure. According to some embodiments, visual, auditory, and haptic feedback may be provided so that the physician does not have to split his or her focus between multiple individual screens showing a post-processed 3D or 4D computed tomography angiography (“CTA”), live transesophageal echocardiography (“TEE”), fluoroscopy, and/or hemodynamics. As the information is being overlaid in XR 3D in space or overlaid with the patient, the physician no longer has to mentally merge these different modalities and can focus more on the procedure, especially in cases where navigating wires, catheters, and devices are a challenge. 
       FIGS.  6 H- 6 L  depict steps for the VFA procedure, in which the EI ecosystem generates 3D XR displays of targets and trajectories (with boundaries having auditory, tactile, and visual feedback), with displays of past tracks and projections of ideal paths, as well as displays of patient data from a database, with multimodal image merging and data overlays (e.g., ECG, doppler, and/or navigation, etc.) while tagging the target.  FIG.  6 H  depicts jugular or femoral access up to the right atrial wall superior to the tricuspid valve showing pathways in multiple planes and axes.  FIG.  6 I  depicts rotation to the triangle of Koch with multi-planar approach angle (i.e., 3D “S” shaped trajectory, or the like).  FIG.  6 I  (right) depicts a visualization of the tricuspid valves (“TV”) and the mitral valves (“MV”) with ICE.  FIG.  6 J  depicts rotation of the implant to anchor leads and to retract the semi-rigid cup/capsule in the tight RA space.  FIG.  6 K  depicts testing of atrium pacing and ventricular pacing, then releasing and retracting the anchor when the rhythm is satisfactory.  FIG.  6 L  depicts test pacing and sensing coupled with AI prescriptive and predictive algorithms before the delivery system is detached. 
     With respect to the VFA procedure, the one or more surgical devices used may include, without limitation, at least one of one or more catheters, one or more catheter interconnect cables, one or more leads, one or more rigid robotic devices, one or more soft robotic devices, one or more robotic systems, one or more robotic arms, one or more handheld robotic systems, one or more robotic systems integrated into a device handle, one or more therapeutic delivery devices, one or more implant delivery devices, one or more diagnostic devices, one or more diagnostic catheters, one or more implant devices, one or more surgical tools, one or more monitoring devices, one or more imaging tools, one or more fiducials, one or more anchors, one or more vascular cannulae, one or more vascular closure tools, one or more guide wires, one or more introducers, one or more sheaths, one or more energy delivery tools, an implantable cardioverter defibrillator (“ICD”) device, an extravascular ICD (“EV-ICD”), a miniature leadless implant, a miniature leadless pacemaker delivery system, a miniature leadless pacemaker, or one or more capital equipment, and/or the like. 
     For VFA, the one or more patient sensor data may be obtained using one or more sensors including, but not limited to, at least one of one or more chronically implanted sensors, one or more diagnostic sensors, one or more surgical sensors, one or more wearable sensors, one or more impedance sensors, one or more ultrasound sensors, one or more electrical sensors, one or more motion sensors, one or more blood pressure sensors, one or more heart rate sensors, one or more pulse sensors, one or more accelerometers, one or more image sensors, one or more ambulatory monitoring sensors, one or more patient mattress sensors, one or more doppler sensors, one or more biomarker sensors, one or more perfusion sensors, one or more electromyography (“EMG”) sensors, one or more electrocardiography (“ECG”) sensors, one or more electromechanical wave imaging (“EWI”) system sensors, one or more electroanatomic mapping (“EAM”) system sensors, one or more cardiac hemodynamics sensors, one or more ischemia sensors, one or more nociception monitoring sensors, or one or more pain sensors, and/or the like. 
     For VFA, the one or more patient imaging data may be obtained using one or more imaging devices including, but not limited to, at least one of a magnetic resonance imaging (“MRI”) system, a diffusion-tensor imaging (“DTI”) system, a computed tomography (“CT”) system, an intraoperative 2D/3D imaging system (“O-Arm”), an ultrasound (“US”) system, a transesophageal echocardiography (“TEE”) system, an intra-cardiac echocardiography (“ICE”) system, a transthoracic echocardiography (“TTE”) system, an intravascular ultrasound (“IVUS”) system, an endobronchial ultrasound system (“EBUS”), an endoscopic ultrasound system (“EUS”), an electromechanical wave imaging (“EWI”) system, a magnetic resonance angiography (“MRA”) system, a computed tomography angiography (“CTA”) system, an optical coherence tomography (“OCT”) system, an optical imaging spectroscopy (“OIS”) system, a magnetic resonance spectroscopy (“MRS”) system, a dynamic susceptibility contrast (“DSC”) MRI system, a fluid-attenuated inversion recovery (“FLAIR”) system, a fluoroscopy system, a biplane fluoroscopic or cineradiographic system, a rotational angiographic system, an X-ray system, a 3D scanning system, an infrared (“IR”) system, an ultraviolet (“UV”) system, a bioluminescent system, an endoscopy system, a triboluminescence system, or an image fusion system, and/or the like. 
     The tracking systems for VFA may include, without limitation, at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system, and/or the like. 
     For VFA, modifications to a miniature leadless device (e.g., implant or pacemaker, etc.) may accomplish sensing in the atrium and pacing in the ventricle, hence a leadless ventricle from atrium sensing and pacing system procedure (also referred to as ventricle from atrial (“VFA” or “VfA”) procedure). However, the AI or EI ecosystem may be built around this device to simplify this procedure to be reliable and repeatable is necessitated to reach fully its potential. The VFA procedure (and many similar cardiac or endoluminal procedures) have many limitations that are addressed by the inventions described herein, where the goal is to ultimately democratize a complex procedure for pre-op, intra-op, and post-op workflow. The AI or EI ecosystem addresses the problems of the conventional treatments for Bradycardia, by: (i) providing procedures with novel workflows that are intuitive and easy to use; (ii) providing enablers for remote management; (iii) providing a way to facilitate or improve differential diagnosis with; (iv) providing a user interface to display proper treatment options; (v) providing guidance as to readmission rates being a problem or not and as to what strategies currently exist to prevent them; and/or (vi) providing guidance as to whether there are any wearable or external devices that are used or investigated for the remote management and prevention; and/or the like. 
     Some unique challenges of the VFA procedure that may be addressed by AI or EI ecosystem may include, without limitation: (a) image or navigation compensation for beating heart, respiration, posture, changes from anesthesia, tissue distortion due to robotic and flexible scopes, static fluoro or CT images, etc. [e.g., using real time registration and tracking in 3D XR, image merging, etc.]; (b) imaging lacking in informing the physician needs for 3D placement in the proper viewing location (e.g., a typical procedure with a catheter the physician is looking away from the patient and device on a screen with no additional information to facilitate the decision making) [e.g., reducing cognitive load and lost switching time with right data, in the right place, at the right time, etc.]; (c) real time placement confirmation of the therapeutic device in the cardiovascular system must be performed to assure safety and efficacy of the procedure; (d) knowing your location and dosing awareness or judging the needed aggressiveness of the lesion or implant; (e) needs to be placed across a heart wall to pace across two chambers (e.g., area of target Triangle of Koch is very small, so system needs to be quite accurate) [e.g., using real time sensing and pacing conduction pathways; this area has a unique EGM signature that an AI feedback loop with predictive and prescriptive input would be beneficial (i.e., predictive analytics interprets trends, whereas prescriptive analytics uses heuristics (rules)-based automation and optimization modeling to determine the best way forward); two distinct electrode contact surfaces and geometries need to be simultaneously deployed and functioning]; (f) CT scan done ahead for critical landmarks [e.g., using image merging and EWI would be ideal]; (g) fluoro and ICE (optional) are typically done for landmarks and placement [e.g., using echogenic and fluoroscopic, as well as tracking via navigation (e.g., RFID, impedance, electromagnetic (“EM”), etc.)]; (h) coronary sinus is key landmark for placement [e.g., locating and tracking the coronary sinus overtime and space]; (i) delivery from multiple locations, such as short distance from jugular vein(s) or long from femoral; (j) solid 2 inch cantilever after delivery system bend (with spacing very constrained (which is more of a limitation than French diameter size)); (k) need to bend 90 degrees and go posterior with tip so it is almost an “S” shape in 3D and need multiple deflections and clocking [e.g., addressing this issue with the following: (1) retraction and rotation of cup doubles the space; (2) one stop versus reimplant (e.g., using pace to make sure meets requirements before final deploy (visualize delivery system and implant in unison)); (3) retrieval after deployment acutely and chronically; (4) optical marking such as with Hugo; and (5) physician perspective as if riding the delivery system]; (m) placement also located between two moving heart valves [e.g., using real time movement of leaflets and blood flow]; (n) efficacy of targeted therapy may be an issue because diversified tissue properties (e.g., calcium, thinning, infarct, etc.); (o) fluoroscopic landmark of the coronary sinus (“CS”) ostium (“OS”) is essential at implant [e.g., marking and tracking critical structures]; (p) target the slow pathway (AV septal wall) between CS OS and tricuspid valve (“TV”) septal annulus; (q) able to pace atrium at low output and pace ventricle at high output due to virtual electrode effect before fixating; (r) paced ECG morphologies should be similar to the intrinsic (Bradycardia) or corrected bundle branch block ECG morphologies [e.g., need simultaneous mechanical and electrical visualization (and flow in some cases)]; (s) perforation into the LV blood pool during fixation can be detected by pacing impedance to enable repositioning; (t) AV Node disruption or block biggest risk; (u) fatigue of helix anchor from movement of leadless device body cantilevered; (v) X-ray exposure to both patient and physician; (w) pediatric anatomy, physiology, and conditions may vary from adult population [e.g., using AI database (feedback mechanism to determine if adequate therapy is achieved acutely and chronically) and 3D XR navigation]; (x) dye and anesthesia exposure to the patient [e.g., reducing anxiety and pain with XR, AI feedback loop, etc.]; and/or the like. 
     Although the  FIGS.  6 A- 6 L  are directed to VFA, the various embodiments are not so limited, and there are several common and unique soft tissue applications that can benefit from the EI ecosystem features and functionalities as described above for VFA, including, but not limited to, (1) Heart Failure (HF) and Pulmonary Artery Hypertension (PAH) applications; (2) structural heart applications; (3) vascular applications; (4) neuro soft tissue or neuro endoluminal applications; and/or the like. 
     For HF and PAH applications, a cardiac shunting procedure (such as described in the &#39;632 and &#39;487 Applications, which have already been incorporated herein by reference in their entireties for all purposes) may be used. Today, no device or drug is approved to treat HFpEF (which occurs for approximately 50% of HF patient population). Standard of Care (“SOC”) is to manage comorbidities. Early clinical data demonstrates that the relief of left atrial pressure using atrial shunting relieves symptoms, reduces need for diuresis, and reduces hospitalizations. Ablating a hole or cut valve may be adopted favorably if the septum hole remains open. Features of the cardiac shunting procedure as described in the &#39;632 and &#39;487 Applications may include, without limitation, pre-operative planning to determine optimal hole size or cut valve and location on septum with simulation results; intra-operative navigation with “beating heart” compensation to locate with precision the septal puncture anatomical site; elimination of the need for an atrial septal shunting device to improve workflow and patient outcomes; and post-operative machine learning feedback looping to pre-operative planning simulation model to ensure predicted results match actual patient outcomes; and/or the like. 
     For structural heart applications (such as described in the &#39;289 and &#39;487 Applications, which have already been incorporated herein by reference in their entireties for all purposes; and also described in C00016316.50), conditions [and their corresponding devices or therapies] may include, but are not limited to, (i) stroke or thromboembolism [left atrial appendage (“LAA”) closure, which may also apply to neuro and vascular applications such as aneurysms); differentiated LAA with gel fill; cryoablation and stapling combination; etc.]; (ii) valve stenosis (e.g., aortic, mitral, tricuspid, pulmonary) [TAVI; etc.]; (iii) valve regurgitation (aortic, mitral, tricuspid, pulmonary) [TAVI; TMVI; adjustable annular rings; etc.]. 
     Technical challenges and/or barriers that may be addressed by AI or EI ecosystem may include, without limitation: (a) blood flow parameters such as doppler (e.g., EOA; jet velocity; pressure drop; etc.); (b) patient population (i.e., anthropometry) differences (e.g., Japan versus Sweden) resulting in access and patient prosthesis mismatch; (c) LAA has highly variable anatomy compared to other cardiac structures with up to 13 devices in one family of devices; (d) physical size limitations (e.g., for TMVI and tricuspid; etc.), with the tricuspid and right side more apt to adversely affect conduction system; (e) tricuspid valve and mitral valve have chordae or papillary, complex annuli, etc.; (f) fixation to tissue (e.g., moving, calcium, thin, friable); (g) pediatric or congenital (with patients having highly variable anatomy and unique physiology) [e.g., growing with patient; regenerative medicine applications; etc.]; (h) Good seal or perivalvular [e.g., acute from chronic ingrowth]; (i) interference of LBBB (TAVI); (j) pericarditis; (k) emboli during procedure [e.g., dislodging calcium or thromboembolic event from ingrowth]; (l) axial and rotational accuracy (clocking) for perfusion, LBB, saddle shaped; (m) ultrasound shadowing (TMVI) [e.g., echogenic designed and AI cleanup]; (n) tortuosity around tight curves; (o) anatomical remodeling of LA or LV from infarcts; (p) X-ray exposure to both patient and physician; (q) dye and anesthesia exposure to the patient; and/or the like. 
     For vascular applications, conditions or applications may include, without limitation, (i) abdominal aortic aneurysm (“AAA”), thoracic aortic aneurysm (“TAA”), thoracoabdominal aortic aneurysm (“TAAA”), complex aortic arch aneurysm, dissection or rupture and transection; (ii) vascular occlusion; (iii) atherectomy; (iv) renal denervation (high blood pressure); (v) distal protection or embolic protection; (vi) deep vein thrombosis (“DVT”); (vii) identification of patients with aortic pathologies; (viii) differentiation of rapid progressors compared with stable disease progression; (ix) modeling method to predict interaction of the device in patient specific anatomies to assess acute and long-term outcomes; (x) provide the opportunity for the physician to practice deploying a device in complex anatomies with feedback on how the device would interact in the anatomy and provide a prediction of outcomes; (xi) generate feedback on forces incurred during the tracking and deployment of devices to reduce vascular trauma; (xii) generate feedback from the delivery system of the presence of friable material that would pose an embolic risk; (xiii) detection of stent graft infection (as indicated by, e.g., temperature change, pH change, antibody screening, etc.) in order to detect early and to treat the infection without removal of the device; (xiv) information on the 3D positioning of devices relative to anatomical structures like branch vessels; (xv) periprocedural feedback on device positioning for optimal long-term performance; (xvi) lower radiation exposure to physicians and patients; (xvii) assessing device movement that does not require contrast or x-ray exposure; (xviii) provide ability to visualize blood flow without the need for contrast agents that incur renal side effects; (xix) utilize predictive techniques (i.e., flow and pressure) to assess implant during index procedure (e.g., seal length, occlusive risk, durability, etc.); (xx) provide early warning of continued disease progression (e.g., inflammation biomarkers, temperature &amp; pH change, increased metabolic demand, etc.) in the landing zones; (xxi) provide ability to assess pressure and flow changes in the aneurysm sac or false lumen; (xxii) lower the burden for invasive and costly follow-up; (xxiii) make it easier to follow-up with patients with less invasive procedures such as ultrasound; (xxiv) enable periprocedural monitoring of blood flows to the spine to predict and prevent spinal cord injury (“SCI”)—flowmet; (xxv) provide non-invasive techniques to monitor biomarkers during follow-up; (xxvi) provide techniques to detect clot maturation in the aneurysm sac or false lumen; (xxvii) provide techniques to detect type of clot (e.g., red, white, relative age, etc.) to help determine best atherectomy device to use (e.g., using a wire with a sensor on the tip, etc.); (xxviii) provide techniques to detect bubbles entrapped in graft cover; (xxix) provide ability to detect a flow disturbance across an implanted graft to prevent occlusion; (xxx) provide computational techniques to triage rupture risk profile for each patient anatomy; (xxxi) provide ability to detect passivation of the implant (e.g., protein absorption and endothelialization); (xxxii) provide ability to predict and detect changes in vascular compliance (e.g., detect changes in afterload of the LV) because of device placement; (xxxiii) provide ability to assess stress changes to stents and/or fabric material; (xxxiv) provide pressure sensing on remodeling balloon to determine maximum fill without risk of barotrauma to vessel or device; (xxxv) provide tissue sensor on EndoAnchor to detect penetration into vessel wall, versus fabric only, or a complete miss; (xxxvi) provide pressure sensors, impedance sensors to detect and display correct angle for HeliFx; (xxxvii) provide periprocedural feedback on ostial encroachment; and/or the like. 
     Technical challenges and/or barriers that may be addressed by AI or EI ecosystem may include, without limitation: (a) vascular size; (b) tortuosity around tight curves [e.g., using real time navigation; seal around lesion]; (c) thin vascular walls and friable tissue and luminal thrombus; (d) calcification [e.g., providing access and navigation through femorals; emboli (e.g., porcelain aorta)]; (e) maintain branch vessel patency like feeder vessels to lumbar (paralysis), which is very difficult to visualize; (f) “Blush” around stent graft because it requires time to develop impermeable clot [e.g., AI may be used to predict tissue ingrowth and computation fluid dynamics (CFD) for placement to fully seal chronically; 3D visualization in XR]; (g) anatomical remodeling during device tracking or application and after grafting [e.g., using AI to predictive model via statistical shape modeling (“SSM”) and finite element analysis (“FEA”), 3D visualization in XR; etc.]; (h) X-ray exposure to both patient and physician; (i) dye and anesthesia exposure to the patient; (j) highly variable vascular anatomy—which requires a large catalogue of implant to cover range of sizes (circumferential and length); (k) variability in sizing of implants, with bias and difference in measurement protocols between hospitals; (l) variability in disease progression making it difficult to properly diagnose and treat; (m) no indication if aneurysm or false lumen was properly and fully sealed off (which, in some cases, must wait ˜30 days); (n) difficult to detect leaks in non-growing (stable) sacs; (o) patient anatomies that are not compatible or amenable to certain device sealing technology or method; and/or the like. 
     For neuro soft tissue or endoluminal applications may include, but are not limited to, (i) thrombectomy; (ii) neuro applications such as aneurysms; (iii) neuro stenting; and/or the like. Technical challenges and/or barriers that may be addressed by AI or EI ecosystem may include, without limitation: (a) simultaneous blood flow and EEG; and/or the like. 
       FIGS.  6 M and  6 N  depict non-limiting examples of images that may be presented on a two-dimensional (“2D”) display and a three-dimensional (“3D”) display, respectively, as part of a display output for an IA or EI ecosystem implementation for soft tissue luminal applications, in accordance with various embodiments. As shown in  FIGS.  6 M and  6 N , the IA or EI ecosystem provides or presents a user or medical professional with XR images (as described in detail above with respect to the embodiments of  FIGS.  1 - 5   , or the like), and may be displayed as a 2D display (e.g., on a laptop, monitor, or other screen display/projection, or the like) (as shown in  FIG.  6 M ) or as a 3D display (e.g., via VR, AR, MR, and/or XR devices (e.g., UX devices), or the like) (as shown in  FIG.  6 N ). Although  FIGS.  6 M and  6 N  depict the room-view, the various embodiments are not so limited, and the IA or EI ecosystem may provide or present the user or medical professional with XR-image-based overlays, cutouts, or other 2D, 3D, and/or 4D representations of the one or more soft tissue luminal portions, targeted portions of the one or more soft tissue luminal portions, trajectories, and planned outlines of the soft tissue luminal procedure, so as to enable the user or medical professional to more easily and accurately achieve miniature leadless device placement, or any other cardiac (or non-cardiac) medical procedures, or the like. 
       FIGS.  7 A- 7 D  (collectively, “ FIG.  7   ”) are flow diagrams illustrating a method  700  for implementing an IA or EI ecosystem for soft tissue luminal applications, in accordance with various embodiments. Method  700  of  FIG.  7 A  continues onto  FIG.  7 B  following the circular marker denoted, “A,” continues onto  FIG.  7 C  following the circular marker denoted, “B,” and/or continues onto  FIG.  7 D  following the circular marker denoted, “C.” 
     While the techniques and procedures are depicted and/or described in a certain order for purposes of illustration, it should be appreciated that certain procedures may be reordered and/or omitted within the scope of various embodiments. Moreover, while the method  700  illustrated by  FIG.  7    can be implemented by or with (and, in some cases, are described below with respect to) the systems, examples, or embodiments  100 ,  200 ,  300 ,  400 ,  500 ,  500 ′, and  600  of  FIGS.  1 ,  2 ,  3 ,  4 ,  5 A,  5 B, and  6   , respectively (or components thereof), such methods may also be implemented using any suitable hardware (or software) implementation. Similarly, while each of the systems, examples, or embodiments  100 ,  200 ,  300 ,  400 ,  500 ,  500 ′, and  600  of  FIGS.  1 ,  2 ,  3 ,  4 ,  5 A,  5 B, and  6   , respectively (or components thereof), can operate according to the method  700  illustrated by  FIG.  7    (e.g., by executing instructions embodied on a computer readable medium), the systems, examples, or embodiments  100 ,  200 ,  300 ,  400 ,  500 ,  500 ′, and  600  of  FIGS.  1 ,  2 ,  3 ,  4 ,  5 A,  5 B, and  6    can each also operate according to other modes of operation and/or perform other suitable procedures. 
     In the non-limiting embodiment of  FIG.  7 A , method  700 , at block  705 , may comprise receiving, using a computing system, one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons and one or more objects within a room, and/or the like. 
     At block  710 , method  700  may comprise receiving, using the computing system, one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient, one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices relative to the one or more portions of the body of the patient and relative to any other surgical devices. 
     Method  700  may further comprise, at block  715 , analyzing, using the computing system, the received one or more first layer input data and the received one or more second layer input. 
     Method  700  may further comprise generating, using the computing system, one or more recommendations for guiding a medical professional in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising three-dimensional (“3D”) or four-dimensional (“4D”) mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient (block  720 ); generating, using the computing system, one or more extended reality (“XR”) images, based at least in part on the generated one or more recommendations (block  725 ); and presenting, using the computing system and using a user experience (“UX”) device, the generated one or more XR images (block  730 ). In some embodiments, the one or more soft tissue luminal portions may include, but are not limited to, at least one of a heart, a lung, a blood vessel, a gastrointestinal (“GI”) tract, or another lumen of the patient, and/or the like. In some instances, the lumen may include, without limitation, one of the heart, the lung, a blood vessel (e.g., the artery, the vein, or a capillary, etc.), the gastrointestinal tract (e.g., the esophagus, the stomach, the small intestine, or the large intestine, etc.), a respiratory tract (e.g., the trachea, bronchi of the lungs, etc.), neurovascular (e.g., artery or vein in the brain, etc.), peripheral vascular (e.g., artery or vein in the extremities, etc.), or a female genital tract, and/or the like. 
     In some embodiments, the computing system may correspond to (or may include) the system hub or computing system  105   a  or  105   b  of system  100  of  FIG.  1   , or the like. According to some embodiments, the one or more devices may correspond to (or may include) the one or more devices or equipment  135  of system  100  of  FIG.  1   , or the like. In some instances, the one or more imaging systems may correspond to (or may include) the one or more imaging devices or systems  140  of system  100  of  FIG.  1   , or the like. In some cases, the one or more imaging systems may correspond to (or may include) the one or more imaging devices or systems  140  of system  100  of  FIG.  1   , or the like. In some instances, the UX device may correspond to (or may include) the UX devices or systems  155  of system  100  of  FIG.  1    and/or the user interface aspects  295  of system  200  of  FIG.  2   , or the like. 
     According to some embodiments, method  700 , at optional block  735  may comprise tracking, using the computing system, the one or more surgical devices, using at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system, and/or the like. 
     Method  700  may continue onto the process at block  740  in  FIG.  7 B  following the circular marker denoted, “A,” may continue onto the process at block  760  in  FIG.  7 C  following the circular marker denoted, “B,” and/or may continue onto the process at block  780  in  FIG.  7 D  following the circular marker denoted, “C.” 
     At block  740  in  FIG.  7 B  (following the circular marker denoted, “A”), method  700  may comprise receiving, using the computing system, one or more control inputs from the medical professional. Method  700  may further comprise, at block  745 , analyzing, using the computing system, the received one or more control inputs in conjunction with analysis of the received one or more first layer input data and the received one or more second layer input data. Method  700 , at block  750 , may comprise generating, using the computing system, one or more control instructions based at least in part on the analysis, the generated one or more control instructions taking into account movement including at least one of movement of one or more soft tissue luminal portions and surrounding tissue due to at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, and/or the like. Method  700  may further comprise sending, using the computing system, the generated one or more control instructions to the robotic system to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient (block  755 ). 
     Alternatively, or additionally, at block  760  in  FIG.  7 C  (following the circular marker denoted, “B”), method  700  may comprise receiving, using the computing system, one or more control inputs from the medical professional, including hand-movement-based control inputs resulting from movement of one or more hands of the medical professional, or the like. Method  700  may further comprise, at block  765 , determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional. If so, method  700 , at block  770 , may comprise generating, using the computing system, one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more control inputs that are based on excessive movement of the at least one hand of the medical professional. Method  700  may further comprise sending, using the computing system, the generated one or more control instructions to a robotic system to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient (block  775 ). 
     Alternatively, or additionally, at block  780  in  FIG.  7 D  (following the circular marker denoted, “C”), method  700  may comprise tracking, using the computing system, the one or more surgical devices (e.g., a miniature leadless device, etc.), as the surgical device(s) (i.e., the miniature leadless device, etc.) is navigated within the body of the patient (e.g., via one of a jugular access or a femoral access, etc.) toward the soft tissue luminal portion (e.g., the heart, etc.) of the patient. Method  700  may further comprise, at block  785 , presenting, using the computing system and using the UX device, the generated one or more XR images to guide, in real-time or near-real-time, the medical professional in positioning the device (i.e., the miniature leadless device, etc.) within one or more predetermined or real-time adjusted targeted locations within the soft tissue luminal portion (e.g., the heart, lung, blood vessel, GI tract, etc.), which is in motion due to expected bodily activity (e.g., cardiac activity, respiratory activity, etc.). In other words, the XR images guide the medical profession, in real-time or near-real-time, to a small moving target that is physically moving and electrically changing (in the case of the heart, etc.) that must be tracked and adjusted in real-time, in some cases, with multiple locations being targeted that are simultaneously and asynchronously in motion. Method  700  may further comprise presenting, using the computing system and using the UX device, the generated one or more XR images to highlight, in real-time or near-real-time, at least one of the one or more targeted locations, one or more guided paths or trajectories toward each of the one or more targeted locations, or one or more portions of the heart, lung, or other organ structures to avoid (block  790 ). In some embodiments, method  700  may further comprise, at block  795 , sending, using the computing system, one or more sets of instructions generated by a programmer system, the one or more sets of instructions being configured to program one or more settings or configurations of the device (e.g., the miniature leadless device, etc.). In some instances, the one or more settings or configurations of the miniature leadless device may include, but are not limited to, at least one of pacing mode, rate limits, stimulation parameters, sensing parameters, rate response parameters, or other parameters related to operation of the miniature leadless device, and/or the like. In some embodiments, the programmer system may also be used to enter patient information, including, but not limited to, at least one of name, identification (“ID”), date of birth, history of clinical conditions, device serial number, physician notes, physician name, physician phone number, or the hospital where the implant occurred, and/or the like. This information may be used to generate a Patient Information Report, which may be entered into electronic health records (“EHR”) or the like. According to some embodiments, the initial device operating parameters—including, but not limited to, at least one of R-wave amplitude in mV, electrode impedance in ohms, the pacing threshold in V, and the stimulation pulse width in msec, and/or the like—may be sent from the device to the programmer system. In some cases, navigating the device (e.g., the miniature leadless device, etc.) within the body of the patient may be performed using one or more robotic systems controlled by one or more control inputs received from the medical professional via the computing system. 
     Exemplary System and Hardware Implementation 
       FIG.  8    is a block diagram illustrating an exemplary computer or system hardware architecture, in accordance with various embodiments.  FIG.  8    provides a schematic illustration of one embodiment of a computer system  800  of the service provider system hardware that can perform the methods provided by various other embodiments, as described herein, and/or can perform the functions of computer or hardware system (i.e., system hubs or computing systems  105   a ,  105   b , and  205 ; mapping and navigation systems (e.g., electroanatomic mapping (“EAM”) system, high-density mapping catheter, patient patches, navigation hardware and software, etc.)  115   a ,  115   b , and  240 ; devices or equipment (e.g., robotics systems, surgical training simulator, electrosurgical generator, radiofrequency (“RF”) ablation generator, phased RF (“pRF”) ablation generator, cryoballoon or cryoablation catheter system, pulsed field ablation (“PFA”) system, a microwave (“MW”) ablation system, monitoring catheter, respiratory equipment, surgical tools, deflectable or steerable sheath, dilator, deployment device, cardiac bionic construct (“CBC”), steering subsystem, handled subsystem, pressure subsystem, coronary sinus (“CS”) catheter, guidewire, introducer sheath, respiratory and other surgical equipment, transseptal needle, syringe and manifold system, etc.)  135 ,  135   a ,  135   b , and  210 ; imaging systems (e.g., computed tomography (“CT”) machine, electrophysiology (“EP”) system, fluoroscopy system, etc.)  140  and  245 ; sensors (e.g., instrumentation, IoT sensors, biometrics system, electrogram (“EGM”) or electrocardiogram (“ECG”) system, camera control unit, monitor, monitoring catheter, etc.)  145  and  250 ; extended reality (“XR”) platforms or hardware  150  and  260 ; user experience (“UX”) devices  155  and  265 ; data analytics or artificial intelligence (“AI”) systems  160   a  and  160   b ; anatomy or tool registration systems  165  and  220 ; cloud storage system  180 ; etc.), as described above. It should be noted that  FIG.  8    is meant only to provide a generalized illustration of various components, of which one or more (or none) of each may be utilized as appropriate.  FIG.  8   , therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner. 
     The computer or hardware system  800 —which might represent an embodiment of the computer or hardware system (i.e., system hubs or computing systems  105   a ,  105   b , and  205 ; mapping and navigation systems  115   a ,  115   b , and  240 ; devices or equipment  135 ,  135   a ,  135   b , and  210 ; imaging systems  140  and  245 ; sensors  145  and  250 ; XR platforms or hardware  150  and  260 ; UX devices  155  and  265 ; data analytics or AI systems  160   a  and  160   b ; anatomy or tool registration systems  165  and  220 ; cloud storage system  180 ; etc.), described above with respect to  FIGS.  1 - 7   —is shown comprising hardware elements that can be electrically coupled via a bus  805  (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors  810 , including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as microprocessors, digital signal processing chips, graphics acceleration processors, and/or the like); one or more input devices  815 , which can include, without limitation, a mouse, a keyboard, and/or the like; and one or more output devices  820 , which can include, without limitation, a display device, a printer, and/or the like. 
     The computer or hardware system  800  may further include (and/or be in communication with) one or more storage devices  825 , which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like. Such storage devices may be configured to implement any appropriate data stores, including, without limitation, various file systems, database structures, and/or the like. 
     The computer or hardware system  800  might also include a communications subsystem  830 , which can include, without limitation, a modem, a network card (wireless or wired), an infra-red communication device, a wireless communication device and/or chipset (such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a WWAN device, cellular communication facilities, etc.), and/or the like. The communications subsystem  830  may permit data to be exchanged with a network (such as the network described below, to name one example), with other computer or hardware systems, and/or with any other devices described herein. In many embodiments, the computer or hardware system  800  will further comprise a working memory  835 , which can include a RAM or ROM device, as described above. 
     The computer or hardware system  800  also may comprise software elements, shown as being currently located within the working memory  835 , including an operating system  840 , device drivers, executable libraries, and/or other code, such as one or more application programs  845 , which may comprise computer programs provided by various embodiments (including, without limitation, hypervisors, VMs, and the like), and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods. 
     A set of these instructions and/or code might be encoded and/or stored on a non-transitory computer readable storage medium, such as the storage device(s)  825  described above. In some cases, the storage medium might be incorporated within a computer system, such as the system  800 . In other embodiments, the storage medium might be separate from a computer system (i.e., a removable medium, such as a compact disc, etc.), and/or provided in an installation package, such that the storage medium can be used to program, configure, and/or adapt a general purpose computer with the instructions/code stored thereon. These instructions might take the form of executable code, which is executable by the computer or hardware system  800  and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer or hardware system  800  (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code. 
     It will be apparent to those skilled in the art that substantial variations may be made in accordance with specific requirements. For example, customized hardware (such as programmable logic controllers, field-programmable gate arrays, application-specific integrated circuits, and/or the like) might also be used, and/or particular elements might be implemented in hardware, software (including portable software, such as applets, etc.), or both. Further, connection to other computing devices such as network input/output devices may be employed. 
     As mentioned above, in one aspect, some embodiments may employ a computer or hardware system (such as the computer or hardware system  800 ) to perform methods in accordance with various embodiments of the invention. According to a set of embodiments, some or all of the procedures of such methods are performed by the computer or hardware system  800  in response to processor  810  executing one or more sequences of one or more instructions (which might be incorporated into the operating system  840  and/or other code, such as an application program  845 ) contained in the working memory  835 . Such instructions may be read into the working memory  835  from another computer readable medium, such as one or more of the storage device(s)  825 . Merely by way of example, execution of the sequences of instructions contained in the working memory  835  might cause the processor(s)  810  to perform one or more procedures of the methods described herein. 
     The terms “machine readable medium” and “computer readable medium,” as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the computer or hardware system  800 , various computer readable media might be involved in providing instructions/code to processor(s)  810  for execution and/or might be used to store and/or carry such instructions/code (e.g., as signals). In many implementations, a computer readable medium is a non-transitory, physical, and/or tangible storage medium. In some embodiments, a computer readable medium may take many forms, including, but not limited to, non-volatile media, volatile media, or the like. Non-volatile media includes, for example, optical and/or magnetic disks, such as the storage device(s)  825 . Volatile media includes, without limitation, dynamic memory, such as the working memory  835 . In some alternative embodiments, a computer readable medium may take the form of transmission media, which includes, without limitation, coaxial cables, copper wire, and fiber optics, including the wires that comprise the bus  805 , as well as the various components of the communication subsystem  830  (and/or the media by which the communications subsystem  830  provides communication with other devices). In an alternative set of embodiments, transmission media can also take the form of waves (including without limitation radio, acoustic, and/or light waves, such as those generated during radio-wave and infra-red data communications). 
     Common forms of physical and/or tangible computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code. 
     Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor(s)  810  for execution. Merely by way of example, the instructions may initially be carried on a magnetic disk and/or optical disc of a remote computer. A remote computer might load the instructions into its dynamic memory and send the instructions as signals over a transmission medium to be received and/or executed by the computer or hardware system  800 . These signals, which might be in the form of electromagnetic signals, acoustic signals, optical signals, and/or the like, are all examples of carrier waves on which instructions can be encoded, in accordance with various embodiments of the invention. 
     The communications subsystem  830  (and/or components thereof) generally will receive the signals, and the bus  805  then might carry the signals (and/or the data, instructions, etc. carried by the signals) to the working memory  835 , from which the processor(s)  805  retrieves and executes the instructions. The instructions received by the working memory  835  may optionally be stored on a storage device  825  either before or after execution by the processor(s)  810 . 
     As noted above, a set of embodiments comprises methods and systems for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications.  FIG.  9    illustrates a schematic diagram of a system  900  that can be used in accordance with one set of embodiments. The system  900  can include one or more user computers, user devices, or customer devices  905 . A user computer, user device, or customer device  905  can be a general purpose personal computer (including, merely by way of example, desktop computers, tablet computers, laptop computers, handheld computers, and the like, running any appropriate operating system, several of which are available from vendors such as Apple, Microsoft Corp., and the like), cloud computing devices, a server(s), and/or a workstation computer(s) running any of a variety of commercially-available UNIX™ or UNIX-like operating systems. A user computer, user device, or customer device  905  can also have any of a variety of applications, including one or more applications configured to perform methods provided by various embodiments (as described above, for example), as well as one or more office applications, database client and/or server applications, and/or web browser applications. Alternatively, a user computer, user device, or customer device  905  can be any other electronic device, such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network (e.g., the network(s)  910  described below) and/or of displaying and navigating web pages or other types of electronic documents. Although the exemplary system  900  is shown with two user computers, user devices, or customer devices  905 , any number of user computers, user devices, or customer devices can be supported. 
     Certain embodiments operate in a networked environment, which can include a network(s)  910 . The network(s)  910  can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available (and/or free or proprietary) protocols, including, without limitation, TCP/IP, SNA™, IPX™ AppleTalk™, and the like. Merely by way of example, the network(s)  910  (similar to network(s)  175  of  FIG.  1   , or the like) can each include a local area network (“LAN”), including, without limitation, a fiber network, an Ethernet network, a Token-Ring™ network, and/or the like; a wide-area network (“WAN”); a wireless wide area network (“WWAN”); a virtual network, such as a virtual private network (“VPN”); the Internet; an intranet; an extranet; a public switched telephone network (“PSTN”); an infra-red network; a wireless network, including, without limitation, a network operating under any of the IEEE 802.11 suite of protocols, the Bluetooth™ protocol known in the art, and/or any other wireless protocol; and/or any combination of these and/or other networks. In a particular embodiment, the network might include an access network of the service provider (e.g., an Internet service provider (“ISP”)). In another embodiment, the network might include a core network of the service provider, and/or the Internet. 
     Embodiments can also include one or more server computers  915 . Each of the server computers  915  may be configured with an operating system, including, without limitation, any of those discussed above, as well as any commercially (or freely) available server operating systems. Each of the servers  915  may also be running one or more applications, which can be configured to provide services to one or more clients  905  and/or other servers  915 . 
     Merely by way of example, one of the servers  915  might be a data server, a web server, a cloud computing device(s), or the like, as described above. The data server might include (or be in communication with) a web server, which can be used, merely by way of example, to process requests for web pages or other electronic documents from user computers  905 . The web server can also run a variety of server applications, including HTTP servers, FTP servers, CGI servers, database servers, Java servers, and the like. In some embodiments of the invention, the web server may be configured to serve web pages that can be operated within a web browser on one or more of the user computers  905  to perform methods of the invention. 
     The server computers  915 , in some embodiments, might include one or more application servers, which can be configured with one or more applications accessible by a client running on one or more of the client computers  905  and/or other servers  915 . Merely by way of example, the server(s)  915  can be one or more general purpose computers capable of executing programs or scripts in response to the user computers  905  and/or other servers  915 , including, without limitation, web applications (which might, in some cases, be configured to perform methods provided by various embodiments). Merely by way of example, a web application can be implemented as one or more scripts or programs written in any suitable programming language, such as Java™, C, C#™ or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming and/or scripting languages. The application server(s) can also include database servers, including, without limitation, those commercially available from Oracle™, Microsoft™, Sybase™, IBM™, and the like, which can process requests from clients (including, depending on the configuration, dedicated database clients, API clients, web browsers, etc.) running on a user computer, user device, or customer device  905  and/or another server  915 . In some embodiments, an application server can perform one or more of the processes for implementing medical assistance technologies, and, more particularly, to methods, systems, and apparatuses for implementing IA or EI ecosystem, and even more particularly, to methods, systems, and apparatuses for implementing extended intelligence ecosystem for soft tissue luminal applications, as described in detail above. Data provided by an application server may be formatted as one or more web pages (comprising HTML, JavaScript, etc., for example) and/or may be forwarded to a user computer  905  via a web server (as described above, for example). Similarly, a web server might receive web page requests and/or input data from a user computer  905  and/or forward the web page requests and/or input data to an application server. In some cases, a web server may be integrated with an application server. 
     In accordance with further embodiments, one or more servers  915  can function as a file server and/or can include one or more of the files (e.g., application code, data files, etc.) necessary to implement various disclosed methods, incorporated by an application running on a user computer  905  and/or another server  915 . Alternatively, as those skilled in the art will appreciate, a file server can include all necessary files, allowing such an application to be invoked remotely by a user computer, user device, or customer device  905  and/or server  915 . 
     It should be noted that the functions described with respect to various servers herein (e.g., application server, database server, web server, file server, etc.) can be performed by a single server and/or a plurality of specialized servers, depending on implementation-specific needs and parameters. 
     In certain embodiments, the system can include one or more databases  920   a - 920   n  (collectively, “databases  920 ”). The location of each of the databases  920  is discretionary: merely by way of example, a database  920   a  might reside on a storage medium local to (and/or resident in) a server  915   a  (and/or a user computer, user device, or customer device  905 ). Alternatively, a database  920   n  can be remote from any or all of the computers  905 ,  915 , so long as it can be in communication (e.g., via the network  910 ) with one or more of these. In a particular set of embodiments, a database  920  can reside in a storage-area network (“SAN”) familiar to those skilled in the art. (Likewise, any necessary files for performing the functions attributed to the computers  905 ,  915  can be stored locally on the respective computer and/or remotely, as appropriate.) In one set of embodiments, the database  920  can be a relational database, such as an Oracle database, that is adapted to store, update, and retrieve data in response to SQL-formatted commands. The database might be controlled and/or maintained by a database server, as described above, for example. 
     According to some embodiments, system  900  might further comprise system hub or computing system  925   a  and corresponding database(s)  930  (similar to system hub or computing system  105   a  and  205 , and corresponding database(s)  110   a  of  FIGS.  1  and  2   , or the like), mapping and navigation system  925   b  (similar to mapping and navigation system  115   a  and  240  of  FIGS.  1  and  2   , or the like), one or more healthcare professionals  935  (similar to healthcare professional(s)  125  of  FIG.  1   , or the like), a subject  940  (similar to subjects  130  and  230  of  FIGS.  1  and  2   , or the like), one or more devices or equipment  945  (similar to devices or equipment  135 ,  135   a ,  135   b , and  210  of  FIGS.  1  and  2   , or the like), one or more imaging systems  950  (similar to imaging system(s)  140  of  FIG.  1   , or the like), one or more sensors  955  (similar to sensors  145  and  245  of  FIGS.  1  and  2   , or the like), an extended reality (“XR”) platform or system  960  (similar to XR platform or systems  150  and  260  of  FIGS.  1  and  2   , or the like), a user experience (“UX”) device  965  (similar to UX devices  155  and  265  of  FIGS.  1  and  2   , or the like), a data analytics or artificial intelligence (“AI”) system  970  (similar to data analytics or AI system  160   a  of  FIG.  1   , or the like), and/or an anatomy or tool registration system  975  (similar to anatomy or tool registration systems  165  and  220  of  FIGS.  1  and  2   , or the like), and/or the like. In some instances, the system hub or computing system  925   a  and corresponding database(s)  930 , the mapping and navigation system  925   b , the one or more healthcare professional(s)  935 , the subject  940 , the one or more devices or equipment  945 , the one or more imaging systems  950 , the one or more sensors  955 , the XR platform or system  960 , the UX device  965 , the data analytics or AI system  970 , or the anatomy or tool registration system  975 , and/or the like, together with the user devices  905   a  and  905   b  may be located or disposed within clinical environment  980 . In some cases, the clinical environment  980  might include, but is not limited to, a clinic, a hospital, an operating room, an emergency room, a physician&#39;s office, or a laboratory, or the like. 
     In some embodiments, the system  900  might further comprise remote system hub or computing system  985   a  and corresponding database(s)  990   a  (similar to system hub or computing system  105   b  and corresponding database(s)  110   b  of  FIG.  1   , or the like), and remote mapping and navigation system  985   b  and corresponding database(s)  990   b  (similar to mapping and navigation system  115   b  and corresponding database(s)  120   b  of  FIG.  1   , or the like), or the like, that communicatively couple to the system hub or computing system  925   a  via network(s)  910 . 
     In operation, system hub or computing system  925   a  or  985   a  (collectively, “computing system” or the like) might receive one or more first layer input data from one or more first devices, the one or more first layer input data comprising at least one of movement data, position data, relative distance data, or externally observable data for each of one or more persons (e.g., subject  940 , healthcare professional(s)  935 , etc.) and one or more objects (e.g., device(s) or equipment  945 , furniture, etc.) within a room (i.e., clinical environment  980 , or the like). In such cases, the one or more first devices may include at least one of imaging system(s)  950 , sensor(s)  955 , and/or the like, that are configured to obtain, capture, or otherwise provide the one or more first layer input data. The computing system might receive one or more second layer input data from one or more second devices, the one or more second layer input data comprising at least one of one or more patient sensor data for monitoring procedure-relevant aspects of a patient (i.e., subject  940 ), one or more patient imaging data for monitoring images of one or more portions of a body of the patient, or one or more navigation and mapping data for monitoring one or more surgical devices (i.e., the devices or equipment  945 , or the like) relative to the one or more portions of the body of the patient, and/or the like. In such cases, the one or more second devices may include at least one of device(s) or equipment  945 , imaging system(s)  950 , sensor(s)  955 , and/or the like, that are configured to obtain, capture, or otherwise provide the one or more second layer input data. 
     The computing system might analyze the received one or more first layer input data and the received one or more second layer input. The computing system might generate one or more recommendations for guiding a medical professional (i.e., healthcare professional(s)  935 , or the like) in navigating therapy or navigating the one or more surgical devices toward, around, through, and/or within one or more soft tissue luminal portions of the patient to perform a soft tissue luminal procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the one or more soft tissue luminal portions of the patient. The computing system might then generate one or more XR images (or one or more XR experiences), based at least in part on the generated one or more recommendations, and might present the generated one or more XR images (or one or more XR experiences) using a UX device  965 . According to some embodiments, the one or more XR images might be dynamic images, which might include an overlay of data models depicting at least one of electrical pulses, blood flow, tissue movement, damage, stress, and/or the like, and thus may not be a still frame in 3D. In some embodiments, the one or more XR images might include, without limitation, at least one of one or more AR images, one or more AR videos, one or more VR images, one or more VR videos, one or more MR images, one or more MR videos, one or more XR images, or one or more XR experiences, and/or the like. 
     According to some embodiments, the generated one or more XR images might be presented to provide one or more of: a guide for a medical professional (e.g., healthcare professional(s)  935 , or the like), a navigation tool during the soft tissue luminal procedure, a proximity detection tool during the soft tissue luminal procedure, a 3D or 4D visualization view of the at least one or more portions of the patient, a 3D or 4D visualization view of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of a digital twin of at least one of a therapeutic tool, a diagnostic tool, or an imaging tool, a heads-up display of at least one of the one or more first layer input data, a heads-up display of at least one of the one or more patient sensor data, a heads-up display of at least one of the one or more patient imaging data, a heads-up display of physiological data of the patient, or a heads-up display of procedure-related data of the patient, and/or the like. In some instances, generating the one or more XR images might comprise combining or mapping the received one or more first layer input data and the received one or more second layer input into a combined 3D or 4D representation, based at least in part on the analysis and the generated one or more recommendations; and generating the one or more XR images based on the combined 3D or 4D representation. 
     In some embodiments, the computing system might track the one or more surgical devices (e.g., devices or equipment  945 , or the like), using at least one of an electropotential-based tracking system, an impedance-based tracking system, an electromagnetic-based tracking system, a magnetic anomaly detection-based tracking system, a radio frequency identification (“RFID”)-based tracking system, a Bluetooth-based tracking system, a wireless-based tracking system, an optical-based tracking system, a laser-based tracking system, an ultrasound (“US”) imaging-based tracking system, a computer vision-based tracking system, a fluoroscopy-based tracking system, an MRI-based tracking system, an accelerometer-based tracking system, a global positioning system (“GPS”)-based tracking system, an infrared (“IR”)-based tracking system, an ultrasonic sound-based tracking system, a piezoelectric-based tracking system, a simultaneous localization and mapping (“SLAM”)-based tracking system, an acoustic-based tracking system, a radar-based tracking system, a feature identification-based tracking system, a machine learning-based tracking system, a predictive tracking system, a prescriptive tracking system, or a near-field communications-based tracking system, and/or the like. 
     According to some embodiments, the computing system might receive one or more control inputs from the medical professional; might analyze the received one or more control inputs in conjunction with analysis of the received one or more first layer input data and the received one or more second layer input data; might generate one or more control instructions based at least in part on the analysis, the generated one or more control instructions taking into account movement including at least one of movement of one or more soft tissue luminal portions and surrounding tissue due to at least one of continual contraction and expansion of the lung, respiration of the patient, beating of the patient&#39;s heart, changes in posture of the body of the patient, movement of the body of the patient due to effects of anesthesia, tissue distortion due to a robotic system, table movement, fluid loss, changes in posture of the body of the patient, or other movement or shifting of at least one portion of the body of the patient, and/or the like; and might send the generated one or more control instructions to the robotic system (which may, in some cases, be included among the device(s) or equipment  945 , or the like) to cause the robotic system to implement the soft tissue luminal procedure within the one or more soft tissue luminal portions of the patient. 
     In some instances, at least the processes of receiving the one or more first layer input data, receiving the one or more second layer input data, analyzing the received one or more first layer input data and the received one or more second layer input, generating the one or more recommendations, generating the one or more XR images, presenting the generated one or more XR images, receiving the one or more control inputs, analyzing the received one or more control inputs, generating the one or more control instructions, and/or sending the generated one or more control instructions may occur in a manner that is at least one of continual, dynamic, feedback-looped, updated, in real-time, or in near-real-time, and/or the like, during the course of the soft tissue luminal procedure. According to some embodiments, real-time display of simulations and interactions in XR for one or more of statistical shape modeling (“SSM”) of anatomy, finite element analysis (“FEA”), electrical wave propagation, computation fluid dynamics, and/or the like (collectively, “analytical tools” or the like), may be coupled with XR and AI to aid in the placement of a heart valve where flow could be predicted (or, in the case of a lead or ablation, where the wave propagation could be simulated) and displayed before the final placement, or the like. 
     In some embodiments, the received one or more control inputs may comprise hand-movement-based control inputs resulting from movement of one or more hands of the medical professional. In such cases, analyzing the received one or more control inputs may comprise determining whether the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the one or more hands of the medical professional. As such, generating the one or more control instructions may comprise, based on a determination that the hand-movement-based control inputs comprise inputs indicative of excessive movement of at least one hand of the medical professional, the computing system generating one or more compensated control instructions that include control instructions that are based on hand-movement-based control inputs while dampening one or more particular control inputs that are based on excessive movement of the at least one hand of the medical professional. 
     These and other functions of the system  900  (and its components) are described in greater detail above with respect to  FIGS.  1 - 7   . 
     While certain features and aspects have been described with respect to exemplary embodiments, one skilled in the art will recognize that numerous modifications are possible. For example, the methods and processes described herein may be implemented using hardware components, software components, and/or any combination thereof. Further, while various methods and processes described herein may be described with respect to particular structural and/or functional components for ease of description, methods provided by various embodiments are not limited to any particular structural and/or functional architecture but instead can be implemented on any suitable hardware, firmware and/or software configuration. Similarly, while certain functionality is ascribed to certain system components, unless the context dictates otherwise, this functionality can be distributed among various other system components in accordance with the several embodiments. 
     Moreover, while the procedures of the methods and processes described herein are described in a particular order for ease of description, unless the context dictates otherwise, various procedures may be reordered, added, and/or omitted in accordance with various embodiments. Moreover, the procedures described with respect to one method or process may be incorporated within other described methods or processes; likewise, system components described according to a particular structural architecture and/or with respect to one system may be organized in alternative structural architectures and/or incorporated within other described systems. Hence, while various embodiments are described with—or without—certain features for ease of description and to illustrate exemplary aspects of those embodiments, the various components and/or features described herein with respect to a particular embodiment can be substituted, added and/or subtracted from among other described embodiments, unless the context dictates otherwise. Consequently, although several exemplary embodiments are described above, it will be appreciated that the invention is intended to cover all modifications and equivalents within the scope of the following claims. 
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