Patent Description:
Cardiac arrhythmias are common medical disorders in which abnormal electrical signals in the heart cause the heart to contract in a suboptimal manner. The resulting abnormal heartbeat, or arrhythmia, can occur in the atria of the heart (e.g., atrial fibrillation (AF)) and/or the ventricles of the heart (e.g., ventricular tachycardia (VT) or ventricular fibrillation (VF)). Treatments for cardiac arrhythmias attempt to address the mechanisms driving sustained and/or clinically significant episodes including, for example, stable electrical rotors, recurring electrical focal sources, reentrant electrical circuits, and/or the like. Left untreated, cardiac arrhythmias may cause serious complications including morbidity (e.g., syncope, stroke, and/or the like) and mortality (e.g. sudden cardiac death (SCD)).

<CIT> discloses a method and an apparatus for localizing the site of origin of an arrhythmia within a body. Hereby, the method according to <CIT> involves applying a multiplicity of recording electrodes to the body. The signals from the recording electrodes are then analyzed to identify the location of the site of origin of the arrhythmia. One or more stimulating electrodes are introduced into the body and are used to stimulate the heart with supra-threshold stimuli. The electrical activity recorded by the recording electrodes is processed to identify the locations of the one or more stimulating electrodes.

<CIT> relates to a method and an apparatus for guiding ablative therapy of abnormal biological electrical excitation. In particular, it is designed for treatment of cardiac arrhythmias. In the method of this invention, electrical signals are acquired from passive electrodes, and an inverse dipole method is used to identify the site of origin of an arrhythmia. The location of the tip of the ablation catheter is similarly localized from signals acquired from the passive electrodes while electrical energy is delivered to the tip of the catheter. The catheter tip is then guided to the site of origin of the arrhythmia, and ablative radio frequency energy is delivered to its tip to ablate the site.

<CIT> discloses a system for computational localization of fibrillation sources in which it is provided. In some implementations, the system performs operations comprising generating a representation of electrical activation of a patient's heart and comparing, based on correlation, the generated representation against one or more stored representations of hearts to identify at least one matched representation of a heart. The operations can further comprise generating, based on the at least one matched representation, a computational model for the patient's heart, wherein the computational model includes an illustration of one or more fibrillation sources in the patient's heart. Additionally, the operations can comprise displaying, via a user interface, at least a portion of the computational model.

It is the object of the present invention to provide an apparatus, a system and a method for improving cardiac arrhythmia source localization.

Systems, methods, and articles of manufacture, including computer program products, are provided for enhanced computational heart simulations. In some example embodiments, there is provided a system that includes at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: receiving, from a first user, clinical data associated with a clinical case; indexing, based at least on a first plurality of characteristics associated with the clinical data, the clinical case, the indexing includes associating at least a portion of the clinical data with a computational simulation of cardiac arrhythmia having a second plurality of characteristics matching the first plurality of characteristics; and responding to a query from a second user by at least sending, to the second user, at least a portion of the clinical data associated with the indexed clinical case.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. The clinical data may include patient anatomic information, diagnostic and/or treatment modalities, treatment parameters, treatment outcome, and medical literature.

In some variations, the first plurality of characteri stics and the second plurality of characteristics may include patient demographics, medical history, and treatment plan.

In some variations, the indexing may include determining, for each of a plurality of computational simulations of cardiac arrhythmias included in a library, a similarity score indicative of a closeness of match between the first plurality of characteristics associated with the clinical data and the second plurality of characteristics associated with each of the plurality of computational models and/or simulations. The indexing may further include associating at least the portion of the data with one of the plurality of computational models and/or simulations having a highest similarity score.

In some variations, at least the portion of the clinical data including the association with the computational simulation of cardiac arrhythmia may be stored at a data store.

In some variations, the query may include a vectorcardiogram (VCG) of a patient. The responding to query may include identifying the computational model of cardiac arrhythmia as most closely matching the vectorcardiogram of the patient and retrieving at least the portion of the clinical data associated with the indexed clinical case in order to send, to the second user, at least the portion of the clinical data.

In another aspect, there is provided a method for enhanced computational heart simulations. The method may include: receiving, from a first user, clinical data associated with a clinical case; indexing, based at least on a first plurality of characteristics associated with the clinical data, the clinical case, the indexing includes associating at least a portion of the clinical data with a computational simulation of cardiac arrhythmia having a second plurality of characteristics matching the first plurality of characteristics; and responding to a query from a second user by at least sending, to the second user, at least a portion of the clinical data associated with the indexed clinical case.

In some variations, the indexing may include determining, for each of a plurality of computational simulations of cardiac arrhythmias included in a library, a similarity score indicative of a closeness of match between the first plurality of characteristics associated with the clinical data and the second plurality of characteristics associated with each of the plurality of computational simulations. The indexing may further include associating at least the portion of the data with one of the plurality of computational simulations having a highest similarity score.

In some variations, the method may further include storing, at a data store, at least the portion of the clinical data including the association with the computational simulation of cardiac arrhythmia.

In another aspect, there is provided a computer program product including a non-transitory computer readable medium storing instructions. The instructions may cause operations may executed by at least one data processor. The operations may include: receiving, from a first user, clinical data associated with a clinical case; indexing, based at least on a first plurality of characteristics associated with the clinical data, the clinical case, the indexing includes associating at least a portion of the clinical data with a computational simulation of cardiac arrhythmia having a second plurality of characteristics matching the first plurality of characteristics; and responding to a query from a second user by at least sending, to the second user, at least a portion of the clinical data associated with the indexed clinical case.

In another aspect, there is provide an apparatus for enhanced computational heart simulations. The apparatus may include: means for receiving, from a first user, clinical data associated with a clinical case; means for indexing, based at least on a first plurality of characteristics associated with the clinical data, the clinical case, the indexing includes associating at least a portion of the clinical data with a computational model and simulation of cardiac arrhythmia having a second plurality of characteristics matching the first plurality of characteristics; and means for responding to a query from a second user by at least sending, to the second user, at least a portion of the clinical data associated with the indexed clinical case.

In another aspect, there is provided a system that includes at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: receiving patient data collected during an electrophysiology procedure; modifying, based at least on the patient data, one or more computational models and/or simulations of cardiac arrhythmia; determining, based at least on the modified one or more computational models and/or simulations of cardiac arrhythmia, a location of a source of the cardiac arrhythmia; and providing an indication of the location of the source of the cardiac arrhythmia to inform treatment based on the patient data.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. The patient data may include at least one of an action potential duration restitution data, conduction velocity restitution data, patient anatomical geometry, voltage mapping, intracardiac ultrasound data, thransthoracic ultrasound data, cone-beam computed tomography data, fluoroscopy data, patient demographics, cardiac activation pattern, regional conduction velocity, and electrogram characteristics.

In some variations, the modifying may include applying, to the one or more computational models and/or simulations, a patient-specific enhancement including at least one of a geometrical morphing and/or rotating, imposing a voltage and/or electrogram information onto the one or more computational simulations, indicating an activation information, adding global and/or regional information regarding a thickness of cardiac structure walls, and incorporating global and/or geographical information regarding the position and morphology of papillary muscles, pulmonary veins, and/or left and right atrial appendages.

In some variations, the modifying may be performed in real time or near real time. The modified one or more computational simulations of cardiac arrhythmia may be returned to a user for clinical use.

In some variations, the one or more computational models and/or simulations may be part of a library of non-patient specific computational simulations of cardiac arrhythmia.

In some variations, an arrhythmia simulation may be initiated based at least on one or more arrhythmia solutions associated with the modified one or more computational simulations of cardiac arrhythmia to generate a patient-tailored arrhythmia vectorcardiogram library for use in a computational arrhythmia mapping process.

In some variations, an arrhythmia simulation may be performed for each of a plurality of source locations based at least on the modified one or more computational simulations of cardiac arrhythmia. The arrhythmia simulation may be performed based on an assumption of the source location. The plurality of source locations and the corresponding arrhythmia simulations may form a patient-tailored arrhythmia library for use in a computational arrhythmia mapping process.

In another aspect, there is provided a method for enhanced computational heart simulations. The method may include: receiving patient data collected during an electrophysiology procedure; modifying, based at least on the patient data, one or more computational models and/or simulations of cardiac arrhythmia; determining, based at least on the modified one or more computational models and/or simulations of cardiac arrhythmia, a location of a source of the cardiac arrhythmia; and providing an indication of the location of the source of the cardiac arrhythmia to inform treatment based on the patient data.

In some variations, the method may further include initiating, based at least on one or more arrhythmia solutions associated with the modified one or more computational simulations of cardiac arrhythmia, an arrhythmia simulation to generate a patient-tailored arrhythmia vectorcardiogram library for use in a computational arrhythmia mapping process.

In some variations, the method may further include performing, for each of a plurality of source locations, an arrhythmia simulation based at least on the modified one or more computational simulations of cardiac arrhythmia. The arrhythmia simulation may be performed based on an assumption of the source location. The plurality of source locations and the corresponding arrhythmia simulations may form a patient-tailored arrhythmia library for use in a computational arrhythmia mapping process.

In another aspect, there is provided a computer program product including a non-transitory computer readable medium storing instructions. The instructions may cause operations which may be executed by at least one data processor. The operations may include: receiving patient data collected during an electrophysiology procedure; modifying, based at least on the patient data, one or more computational simulations of cardiac arrhythmia; determining, based at least on the modified one or more computational simulations of cardiac arrhythmia, a location of a source of the cardiac arrhythmia; and providing an indication of the location of the source of the cardiac arrhythmia to inform treatment based on the patient data.

In another aspect, there is provided an apparatus for enhanced computational heart simulations. The apparatus may include: means for receiving patient data collected during an electrophysiology procedure; means for modifying, based at least on the patient data, one or more computational simulations of cardiac arrhythmia; means for determining, based at least on the modified one or more computational models and simulations of cardiac arrhythmia, a location of a source of the cardiac arrhythmia; and means for providing an indication of the location of the source of the cardiac arrhythmia to inform treatment based on the patient data.

In another aspect, there is provided a system that includes at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: determining, in an electroanatomic map, a location of each of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses; identifying, for each of the n-quantity of pacing sites, a computational model and arrhythmia simulation associated with a vectorcardiogram that matches a patient vectorcardiogram collected while pacing at each of the n-quantity of pacing sites and selecting one or more corresponding pacing sites in the computational model; aligning, based at least on the t location of each of the n-quantity of pacing sites in the electroanatomic map and the computational model, the electroanatomic map and the computational model; and generating, based at least on the aligning, an indication of a location of a source of a clinically relevant cardiac arrhythmia in the computational model relative to the location of each of the n-quantity of pacing sites.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. The n-quantity of pacing sites may include at least three pacing sites.

In some variations, the aligning may include applying a transformative matrix to align a first reference coordinate system of the electroanatomic map and a second reference coordinate system of the computational simulation.

In some variations, the location of the source of the clinically relevant cardiac arrhythmia may be further translated into an electroanatomic mapping system based at least on a prolate spheroidal coordinate system.

In some variations, a treatment may be applied, based at least on the indication, to the location of the source of the clinically relevant cardiac arrhythmia. The treatment may include at least one of an ablation, targeted gene therapy, radiation therapy, and surgical intervention.

In some variations, the computational model, the electroanatomic map, and a mapping result with the n-quantity pacing sites aligned may be displayed.

In another aspect, there is provided a method for enhanced computational heart simulations. The method may include: determining, in an electroanatomic map, a location of each of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses; identifying, for each of the n-quantity of pacing sites, a computational model and arrhythmia simulation associated with a vectorcardiogram that matches a patient vectorcardiogram collected while pacing at each of the n-quantity of pacing sites and selecting one or more corresponding pacing sites in the computational model; aligning, based at least on the location of each of the n-quantity of pacing sites in the electroanatomic map and the computational model, the electroanatomic map and the computational model; and generating, based at least on the aligning, an indication of a location of a source of a clinically relevant cardiac arrhythmia in the computational model relative to the location of each of the n-quantity of pacing sites.

In some variations, the method may further include displaying the computational model, the electroanatomic map, and a mapping result with the n-quantity pacing sites aligned.

In another aspect, there is provided a computer program product including a non-transitory computer readable medium storing instructions. The instructions may cause operations may executed by at least one data processor. The operations may include: determining, in an electroanatomic map, a location of each of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses; identifying, for each of the n-quantity of pacing sites, a computational model and arrhythmia simulation associated with a vectorcardiogram that matches a patient vectorcardiogram collected while pacing at each of the n-quantity of pacing sites and selecting one or more corresponding pacing sites in the computational model; aligning, based at least on the location of each of the n-quantity of pacing sites in the electroanatomic map and the computational model, the electroanatomic map and the computational model; and generating, based at least on the aligning, an indication of a location of a source of a clinically relevant cardiac arrhythmia in the computational model relative to the location of each of the n-quantity of pacing sites.

In another aspect, there is provided an apparatus for enhanced computational heart simulations. The apparatus may include: means for determining, in an electroanatomic map, a location of each of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses; means for identifying, for each of the n-quantity of pacing sites, a computational model and/or arrhythmia simulation associated with a vectorcardiogram that matches a patient vectorcardiogram collected while pacing at each of the n-quantity of pacing sites and selecting one or more corresponding pacing sites in the computational model; means for aligning, based at least on the t location of each of the n-quantity of pacing sites in the electroanatomic map and the computational model, the electroanatomic map and the computational model; and means for generating, based at least on the aligning, an indication of a location of a source of a clinically relevant cardiac arrhythmia in the computational model relative to the location of each of the n-quantity of pacing sites.

Implementations of the current subject matter can include systems and methods consistent including one or more features are described as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations described herein. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a computer-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connection including, for example, a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), a direct connection between one or more of the multiple computing systems, and/or the like.

Other features and advantages of the subject matter described herein may be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to computational heart simulations, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.

Cardiac arrhythmias (e.g., atrial fibrillation, ventricular tachycardia, ventricular fibrillation) may be treated by targeting the mechanisms driving sustained and/or clinically significant episodes including, for example, stable electrical rotors, recurring electrical focal sources, reentrant electrical circuits, and/or the like. Ablation is one example treatment for cardiac arrhythmias in which radiofrequency, cryogenic temperatures, ultrasound, and/or radiation (e.g. stereotactic ablative radiotherapy (SAbR)) may be applied to the source of the cardiac arrhythmia. The resulting lesions may alleviate cardiac arrhythmia by disrupting and/or eliminating the erratic electric signals causing the abnormal heart activation. Nevertheless, the outcome of ablation may depend on a variety of factors including a correct localization of the source of cardiac arrhythmia. With existing methodologies, correctly localizing the source of cardiac arrhythmia remains a challenge. Moreover, the absence of sufficient access to prior clinical case data, including relevant patient anatomy, treatment parameters, and treatment outcome, may be further disadvantage some practitioners treating patients for cardiac arrhythmias. As such, various implementations of the current subject matter include techniques for enhancing computational heart simulations to improve cardiac arrhythmia source localization to facilitate diagnosis and targeted treatment.

In some example embodiments, a library including a plurality of computational models and/or simulations of cardiac arrhythmias (described in <CIT> "Computational Localization of Fibrillation Sources") may be enhanced with clinical data associated with clinical cases. For example, a first user may send, to a data controller associated with the library, clinical data associated with a clinical case that includes patient anatomic information, data such as voltage maps or electrogram characteristics, diagnostic and/or treatment modalities, treatment parameters, treatment outcome, relevant medical literature, and/or the like. The contents of the library may be indexed based on the specific characteristics of the computational models and simulations of cardiac arrhythmia. For instance, upon receiving the clinical data from the first user, the controller may be configured to index the clinical case by at least identifying, in the library, a computational model and simulations matching the clinical case and associating the corresponding clinical data with the matching computational model and simulations. A second user treating a patient for cardiac arrhythmia may, by querying the library based on patient data, gain access to not only matching computational simulations of cardiac arrhythmia but also relevant clinical data including, for example, patient anatomic information, diagnostic and/or treatment modalities, treatment parameters, treatment outcome, relevant medical literature, and/or the like.

In some example embodiments, non-patient specific computational models and simulations of cardiac arrhythmias included in the library may be enhanced using patient data collected during an electrophysiology (EP) study including, for example, action potential duration (APD) restitution data, conduction velocity restitution data, patient anatomical geometry, voltage mapping, intracardiac ultrasound data, transthoracic ultrasound data, conventional computed tomography (CT) data, cone-beam computed tomography (CT) data, positron-emission tomography (PET) scan data, fluoroscopy, magnetic resonance imaging data, patient demographics, cardiac activation pattern, regional conduction velocity, electrogram analysis, and/or the like. For example, the controller coupled with the library may be configured to modify, based at least on the patient data, one or more of the non-patient specific computational simulations included in the library. The modification may be performed in real time (or near real time) such that the modified library of computational simulations may be available when the patient is treated for their cardiac arrhythmia. For instance, localization of the source of a cardiac arrhythmia may be performed based on the modified library of computational simulations before ablation is performed at the source of the arrhythmia.

A computational model of a patient's anatomy, such as a computational representation of the patient's heart, is used to provide supplemental information for a treatment, such as an ablation targeting the source of a cardiac arrhythmia in the patient's heart. Although the computational mapping result may visually identify the location of the source of cardiac arrhythmia within the computational model, the precise relationships between the computational model, the electroanatomic map of the heart, and the patient's actual anatomy may be unclear. As such, in some example embodiments, the computational model of the patient's anatomy may be aligned with the electroanatomic map, first, by tracking a position of one or more catheters, pacemaker leads, or implantable cardioverter defibrillator (ICD) leads relative to the patient's anatomy. Next, the location of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses is identified both in the computational model and in the electroanatomic map to provide n-quantity reference locations in order to align the computational model and the electroanatomic map. Finally, ablation may be performed at the source of the cardiac arrhythmia (e.g. ventricular fibrillation, as determined by the computational model mapping result), with positional reference to the position of the n-quantity of pacing sites identified in the computational simulation of the patient's anatomy.

<FIG> depicts a system diagram illustrating an example of a cardiac arrhythmia control system <NUM>, in accordance with some example embodiments. Referring to <FIG>, the cardiac arrhythmia control system <NUM> may include a data controller <NUM> and a data store <NUM>. As shown in <FIG>, the data controller <NUM> and the data store <NUM> may be communicatively coupled via a network <NUM>. Moreover, <FIG> shows the data controller <NUM> as being communicatively coupled, via the network <NUM>, to one or more clients including, for example, a first client 140a associated with a first user 145a, a second client 140b associated with a second user 145b, and/or the like. The first user 145a at the first client 140a and the second user 145b at the second client 140b may access, via the data controller <NUM>, the contents of the data store <NUM>, which may include a library <NUM> of computational simulations of cardiac arrhythmias. It should be appreciated that various techniques may be applied in order to securitize and/or anonymize the data that is stored and/or transmitted within the cardiac arrhythmia control system <NUM> including, for example, access control, encryption, blockchain, and/or the like.

In some example embodiments, the computational model and library <NUM> of computational simulations of cardiac arrhythmias may be enhanced with clinical data associated with clinical cases. To further illustrate, <FIG> depicts a flow diagram illustrating an example of a data flow <NUM> in the cardiac arrhythmia control system <NUM>, in accordance with some example embodiments. Referring to <FIG>, the first user 145a at the first client 140a may send, to the data controller <NUM>, clinical data associated with a clinical case including, as shown in <FIG>, patient anatomic information 210a, diagnostic and/or treatment modalities 210b, treatment parameters 210c, clinical outcomes 210d, relevant medical literature 210e, and/or the like.

Referring again to <FIG>, examples of patient anatomic information 210a may include cardiac geometry, scar and fibrosis locations, thorax anatomy and pathophysiology and/or the like. Patient anatomic information 210a may be captured in imaging studies obtained prior to and/or during the clinical case. Alternatively and/or additionally, patient anatomic information 210a may be captured during an electroanatomic mapping procedure. The patient anatomic information <NUM> may be loaded into the library <NUM> by loading, for example, raw imaging information including, for example, text files containing data of the patient information (e.g. output data files containing electrogram information from the electrophysiology recording system), intracardiac ultrasound images, transthoracic ultrasound images, computed tomography (CT) images, <NUM>-dimensional computed tomography videos, magnetic resonance imaging (MRI) images, Myocardial Perfusion Imaging tests (MIBI), positron-emission tomography (PET) images, radiographs, and/or the like. Tomographic images may use a spectrum of interpretation from manual interpretation to automated <NUM>-dimensional image creation and analysis.

Moreover, loading the patient anatomic information <NUM> into the library <NUM> may include importing digital information from <NUM>-dimensional electroanatomic mapping systems such as geometry, catheter position, voltage maps, activation data, and analytic data. <FIG> depicts an example of a voltage map <NUM> indicating a relationship between voltage and scar/fibrosis density at various locations across a left ventricle and a right ventricle of a patient's heart. It should be appreciated that at least a portion of the patient anatomic information 210a may include annotations provided by the first user 145a. <FIG> depicts an example of a user interface <NUM> generated by the data controller <NUM>. The user interface <NUM> may be displayed, for example, at the first client 140a in order to receive, from the first user 145a, one or more inputs corresponding to an interpretation of the geometry, orientation, voltage, activation, and analytic information from electroanatomic mapping systems.

Examples of diagnostic and/or treatment modalities 210b may include imaging technology (e.g., fluoroscopy, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron-emission tomography (PET) and/or the like), sheaths (e.g., pre-formed, steerable, and/or the like), mapping catheters (e.g., multi-electrode catheters), and ablation catheters (e.g., solid or irrigated, <NUM> or <NUM> tip size, and/or the like). In some example embodiments, the data controller <NUM> may generate a user interface including a dropdown menu (or another type of graphical user interface element) configured to enable the first user 145a to input the diagnostic and/or treatment modalities that were applied in the clinical case. Alternatively and/or additionally, the data controller <NUM> may receive, from the first user 145a, a scan identifying one or more products used during the clinical case including, for example, a product barcode (e.g. barcode from the box containing the ablation catheter used in the case), an image, and/or the like.

Examples of treatment parameters 210c may include parameters associated with ablation such as, for example, ablation power, location, duration of lesion placement, and the dimension and/or shape of the lesion. For example, digital information including one or more of the treatment parameters 210c may be exported from an electroanatomic mapping system and uploaded to the library <NUM>. <FIG> depicts an example of an electroanatomic map showing, as dots, locations of ablation lesions. Alternatively and/or additionally, the data controller <NUM> may generate a user interface, which may be displayed at the first client 140a in order to receive, from the first user 145a, one or more inputs corresponding to an interpretation of the ablation power, location, duration of lesion placement, and the dimension and/or shape of the lesion.

Additional examples of treatment parameters 210c may include parameters associated with stereotactic ablative radiotherapy (SAbR) such as, for example, target contouring data, internal treatment volume (ITV), planning treatment volume (PTV), radiation dose, radiation energy/delivery time, avoidance structures, respiratory and cardiac motion gating parameters, patient positioning and/or restraining devices, use of paralyzing agents by anesthesia during therapy, pacemaker or implantable cardioverter-defibrillator (ICD) programming parameters, cardiac rhythm during therapy, arrhythmia mapping technology, associated computed tomography (CT) imaging data, associated magnetic resonance imaging (MRI) imaging data, associated ultrasound imaging and tracking data, medications, antiarrhythmic drug therapy, anticoagulation medical therapy, clinical outcomes, complications, and adverse events. For example, digital information including one or more of the treatment parameters 210c may be exported from an stereotactic ablative radiotherapy (SAbR) planning system and uploaded to the library <NUM>. <FIG> depicts an example of planning software for stereotactic ablative radiotherapy (SAbR) showing target volumes, avoidance structures, and computed radiotherapy dosages. Alternatively and/or additionally, the data controller <NUM> may generate a user interface, which may be displayed at the first client 140a in order to receive, from the first user 145a, one or more inputs corresponding to an interpretation of the targeting contouring data, internal treatment volume (ITV), planning treatment volume (PTV), radiation dose, radiation energy/delivery time, avoidance structures, respiratory and cardiac motion gating parameters, patient positioning and/or restraining devices, use of paralyzing agents by anesthesia during therapy, pacemaker or implantable cardioverter-defibrillator (ICD) programming parameters, cardiac rhythm during therapy, arrhythmia mapping technology, associated computed tomography (CT) imaging data, associated magnetic resonance imaging (MRI) imaging data, associated ultrasound imaging and tracking data, medications, antiarrhythmic drug therapy, anticoagulation medical therapy, clinical outcomes, complications, and adverse events.

The clinical results 210d may include results associated with the ablation having the treatment parameters 210c including, for example, acute ablation success (e.g. ablation terminating the arrhythmia, ablation rendering the arrhythmia non-inducible, <NUM> month clinical outcome, and/or the like) and complications. Examples of the relevant medical literature 210e shown in <FIG> may include guidelines, clinical trials, expert opinions, and case reports that are relevant to the clinical case, and indexed according to the parameters of the computational model and arrhythmia simulation library (e.g. arrhythmia type, patient cardiac geometry and scar configuration, arrhythmia source location, etc.). The data controller <NUM> may generate a user interface including a dropdown menu (or another type of graphical user interface element) configured to enable the first user 145a to select one or more inputs from a selection of clinical results.

In some example embodiments, the data controller <NUM> may be configured to index the clinical data received from the first user 145a such that the clinical data may be accessible, for example, to the second user 145b at the second client 140b. The data controller <NUM> may be configured to index, based least on the computational simulations included in the library <NUM>, the clinical data received from the first user 145a such that the clinical data is associated with a computational simulation that most closely matches the corresponding clinical case. For example, each computational simulation included in the library <NUM> may be associated with one or more characteristics including, for example, heart size, shape, presence or absence of structural heart disease, arrhythmia type, and/or the like. Upon receiving the clinical data associated with the clinical case from the first user 145a, the data controller <NUM> may be configured to compute, for each computational simulation in the library <NUM>, a similarity score indicative of a closeness of a match between the respective characteristics of the clinical case and the computational model and simulation library <NUM>. The clinical data associated with the clinical case may be indexed based on the computational model having a highest similarity score. That is, the clinical data associated with the clinical case may be associated with the computational model and/or simulations whose characteristics (e.g., heart size, shape, presence or absence of structural heart disease, arrhythmia type, and/or the like) most closely match those in the clinical case.

The contents of the simulation library <NUM>, including the computational model and/or simulations that have been enhanced with clinical data associated with matching clinical cases, may be accessible to the second user 145b at the second client 140b. For example, the second user 145b may query the library <NUM> in order to identify relevant clinical cases. In some example embodiments, the second user 145b may be treating a patient for a cardiac arrhythmia and may thus query the library <NUM> based on patient data including, for example, patient age, medical history, arrhythmia type, proposed treatment plan, and/or the like. The data controller <NUM> may respond to the query from the second user 145b by at least identifying one or more clinical cases included in the library <NUM> that match the parameters of the query from the second user 145b. Alternatively and/or additionally, instead of one or more specific clinical cases, the second user 145b may apply a collection of clinical cases from the library <NUM> as training data to train a machine learning model to perform a variety of cognitive tasks including, for example, determining the statistical probability of the arrhythmia source location, performing a probabilistic analysis of potential clinical outcomes associated with different treatment approaches (e.g. ablation lesion location and/or target volume, number, and pattern) for arrhythmia, and/or the like.

In some example embodiments, the machine learning model may include a neural network such as, for example, an autoencoder and/or the like. The machine learning model may be trained based on training data that includes clinical data from a large number of patient cases which may be collected and entered as input into the machine learning model. Training data may include patient demographic information, electrocardiographic (ECG) and vectorcardiographic (VCG) tracings, and ground truth labels including the identified arrhythmia source locations. Arrhythmia source locations may be further labeled with ablation site, size, volume, and technique (e.g., catheter ablation versus stereotactic ablative radiotherapy), and some ranking of the outcome (e.g. arrhythmia termination, acute ablation success, long-term ablation success, etc.). Moreover, the machine learning model may be trained to examine features present in the treatment approach for each patient (ablation lesion number, size, volume, configuration, therapy dose, etc.). Additionally, the machine learning model may be trained to determine a similarity metric between different clinical cases based on demographics, arrhythmia type, cardiac anatomy, etc. to determine relevance to both other training case data and/or future cases for comparison. When a user wants to utilize the trained machine learning model, the user may provide, as inputs to the trained machine learning model, a patient's electrocardiogram (ECG) or vectorcardiogram (VCG) as well as one or more patient characteristics and arrhythmia characteristics. The trained machine learning model may determine, based at least on the inputs, a statistical probability of the arrhythmia source location, and a probabilistic analysis of potential clinical outcomes associated with different treatment approaches (e.g. ablation lesion location, number, volume, configuration, therapy dose, etc.) for arrhythmia. In those instances, the data controller <NUM> may also be configured to identify, based at least on the output of the trained machine learning model, a selection of relevant clinical cases for case reference and procedural planning.

In some example embodiments, non-patient specific computational models and arrhythmia simulations included in the library <NUM> may be enhanced using patient data collected during an electrophysiology study (EPS) either in the electrophysiology laboratory, the radiation medicine suite, or operating room (OR) including, for example, action potential duration (APD) restitution data, conduction velocity restitution data, patient anatomical geometry, voltage mapping, intracardiac ultrasound data, transthoracic ultrasound data, conventional computed tomography data, cone-beam computed tomography data, <NUM>-dimensional computed tomography date (<NUM>-D CT), magnetic resonance imaging (MRI) data, positron-emission tomography (PET) data, patient demographics, cardiac activation pattern, regional conduction velocity, electrogram analysis, and/or the like. For example, the data controller <NUM> may be configured to modify, based at least on the patient data, one or more of the non-patient specific computational models and arrhythmia simulations included in the library <NUM>. In this example, the patient's left ventricular geometry and voltage map generated during an ablation case by the electroanatomic mapping system is exported to a USB memory stick and uploaded to the algorithm. The cardiac model is updated to include the information regarding left ventricular size, orientation, and the locations of normal tissue, scar tissue, and fibrosis. Next, previously-computed voltage solutions of cardiac arrhythmias are then incorporated into the updated cardiac model and the solutions run forward in time to compute the vectorcardiogram (VCG) library for the patient, with one or more VCG loops associated with each possible location of the cardiac arrhythmia source. The adjusted VCG library and associated location and other associated metadata are then returned to the clinical user to aid in the clinical case being performed. The modifications may be performed in real time (or near real time) such that the modified library <NUM> of computational simulations may be available when the patient is treated for their cardiac arrhythmia. For instance, localization of the source of ventricular fibrillation may be performed based on the modified library <NUM> of computational simulations before ablation is performed at the source of ventricular fibrillation.

<FIG> depicts a flowchart illustrating an example of a process <NUM> for modifying a library of non-patient specific computational simulations, in accordance with some example embodiments. As shown in Box A, the patient is brought into the electrophysiology laboratory, radiation medicine suite, or operating room, placed on the procedural table, and the procedure is begun (the electrophysiology study (EPS) environment is represented by the bottom portion of <FIG>). Next, as described in Box B. , a patient cardiac geometry is created using a combination of noninvasive techniques (e.g. transthoracic ultrasound, fluoroscopy, cone-beam computed tomography scan, magnetic resonance imaging, etc.) and/or invasive techniques (e.g. invasive electrophysiology catheters are placed in and maneuvered throughout the heart). The cardiac geometry is supplemented by APD restitution data, conduction velocity restitution data, voltage mapping data, intracardiac ultrasound data, patient demographic data [age, weight, height, ejection fraction], cardiac activation pattern, regional conduction velocity, and electrogram analysis. Next, these data are collected and securely exported to high-performance computing resources for analysis (area represented by the top of <FIG>). In this environment, a pre-existing, non-patient-specific library of computational simulations of cardiac arrhythmias (Box <NUM>) is rapidly scaled and adjusted according to the exported data (Box <NUM>). In one example, the cardiac model is updated to include the information regarding left ventricular size, orientation, and the locations of normal tissue, scar tissue, and fibrosis. Next, previously-computed voltage solutions of cardiac arrhythmias are then incorporated into the updated cardiac model and the solutions run forward in time to compute the vectorcardiogram (VCG) library for the patient, with one or more VCG loops associated with each possible location of the cardiac arrhythmia source. As shown in Box <NUM>, the adjusted library, voltage solutions, and arrhythmia source locations are returned to the local electrophysiology laboratory, radiation medicine suite, or operating room (OR) mapping system for patient arrhythmia mapping. Meanwhile in the clinical case (Box C), arrhythmia induction is attempted, if necessary. Arrhythmia electrograms are saved and exported for analysis. Diagnostic catheters are removed (if present) and either ablation catheters are placed within the heart, the stereotactic ablative radiotherapy (SAbR) plan is reviewed, or the surgical plan is evaluated. In Box D, arrhythmia source mapping is performed using the modified VCG library from Box <NUM>. Arrhythmia source locations (the results of the computational mapping process) are displayed for interpretation of the physician. Informed by the mapping results, catheter ablation, stereotactic ablative radiotherapy, or surgical interruption of the arrhythmia sources is begun (Box E).

Referring to <FIG>, the data controller <NUM> may be configured to modify the library <NUM> based on patient data collected during an electrophysiology (EP) study, as noted in <FIG>, including patient demographics, and information derived by positioning one or more catheters in a patient's heart or from interrogation of an implanted pacemaker or an implantable cardioverter-defibrillator (ICD) in order to collect patient-specific data such as, for example, action potential duration (APD) restitution data, conduction velocity restitution data, patient anatomical geometry, voltage mapping, intracardiac ultrasound data, cardiac activation pattern, regional conduction velocity, electrogram analysis, and/or the like. The data controller <NUM> may modify the library <NUM>, including by applying one or more patient-specific corrections, such that one or more of the computational simulations included in the library <NUM> better conform to patient specific characteristics.

To further illustrate, <FIG> depict examples of data collected during an electrophysiology procedure, in accordance with some example embodiments. For example, <FIG> depicts an example of a result of single extrastimulus pacing in the atria, which may illustrate atrial action potential duration (APD) restitution and activation latency. The action potential duration (APD) restitution and activation latency shown in <FIG> may be used to determine the correct parameters for more accurate simulation of atrial arrhythmias within the patient's heart.

<FIG> depicts an example of endocardial geometries and voltage maps in a patient with nonischemic cardiomyopathy and ventricular arrhythmias. A significant amount of data relevant to the arrhythmia simulation process may be created during the electrophysiology mapping process using the <NUM>-dimensional electroanatomic mapping system. For example, cardiac geometry and orientation may be obtained by moving electrophysiology catheters within the heart. The collection of points occupied by such catheters may be used to generate endocardial and epicardial surfaces of the heart shown, for example, in the endocardial geometries of the left (geometry on left side of FIG. ) and right ventricles (geometry on right side of FIG. ) shown in <FIG>.

<FIG> depicts an example of an intracardiac echocardiogram (ICE) image of a left ventricle, which the endocardial surface (bottom arrow) and a basket of catheter spline (top arrow). An intracardiac echocardiography (ICE) system or a transthoracic echocardiography system may collect dynamic, high resolution data regarding the thickness of cardiac walls, the position and thickness of various structures (e.g., papillary muscles, pulmonary vein, left and right atrial appendages), and the positions of other mapping and ablation catheters. The resulting echocardiographic images, such as the one shown in <FIG>, may therefore be used to further refine one or more non-patient specific computational simulations to conform to patient specific cardiac characteristics, as illustrated by the process <NUM> of <FIG> and described above.

In some example embodiments, patient data may be exported from the electroanatomic mapping system and transferred directly to the data controller <NUM>. For example, patient geometry, voltage map, activation map, and electrogram morphology map may be saved a data file (e.g., to a universal serial bus (USB) memory stick, a compact disk (CD), a digital versatile disk (DVD), and/or the like) before being uploaded to the data controller <NUM>. Alternatively, when direct export is not practicable, the data controller <NUM> may provide, for example, via a graphical user interface, a user-editable cardiac model and customizable tools for geometrical morphing and rotation, imposing a voltage and/or electrogram information onto the computational model and arrhythmia simulation, and indicating activation information. Global and/or regional information regarding the thickness of cardiac structure walls as well as the position and morphology of papillary muscles, pulmonary veins, and the left and right atrial appendages may also be incorporated into the model either by morphing the geometry or setting the wall thicknesses. It should be appreciated that various techniques may be applied in order to securitize and/or anonymize the data that is transmitted to and from the data controller <NUM> including, for example, access control, encryption, blockchain, and/or the like.

In some example embodiments, upon receiving the patient data, the data controller <NUM> may be configured to modify, based at least on the patient data, one or more computational models and arrhythmia simulations in the library <NUM> in real time or near real time. As noted, the modifications may include one or more patient specific corrections such that the computational simulations in the library <NUM> better conform to patient specific characteristics. For example, the data controller <NUM> may be configured to fit geometric data to a computational mesh such that the mesh relationships may be used to compute arrhythmia simulations for the patient of interest. The data controller <NUM> may also introduce, into the more patient specific model, voltage solutions of previously simulated rotors and focal sources. A computational simulation may then proceed forward in time to allow arrhythmia maturation and permutations to be recorded (e.g. for several seconds of simulated time). From the computational voltage solutions, the data controller <NUM> may compute and record vectorcardiography (VCG) data, which may be indexed to a source location of the cardiac arrhythmia. Alternatively and/or additionally, the computational renderings of voltage solutions may be performed and recorded as a resource for arrhythmia mapping and validation (e.g. the technology of <CIT> "Computational Localization of Fibrillation Sources").

The data controller <NUM> may, as noted, modify the library <NUM> in real time (or near real time) such that the modified library <NUM> of computational simulations may be available when the patient is treated for ventricular fibrillation. For example, the computed vectorcardiograms and associated source locations, along with the rendered voltage solutions may be encrypted and sent to the first client 140a and/or the second client 140b to at least enable the first user 145a and/or the second user 145b to determine the location of the patient's ventricular fibrillation before performing a treatment such as, for example, ablation at the source of ventricular fibrillation. It should be appreciated that the modified library <NUM> may enable the first user 145a and/or the second user 145b to perform a higher fidelity localization of the source of cardiac arrhythmia, thereby improving the clinical outcome of the treatment targeting the source of cardiac arrhythmia.

In some example embodiments, the data controller <NUM> is configured to align the computational model used for arrhythmia simulation and computational arrhythmia mapping with a <NUM>-dimensional electroanatomic map tracking the positions of one or more catheters relative to the patient's anatomy. This could be accomplished with the following workflow: First, an n-quantity of pacing maneuvers is performed within the patient's heart using either a steerable catheter or pacing electrodes of a pacemaker or implantable cardioverter-defibrillator. Next, the sites of pacing are recorded within the patient's heart using a <NUM>-dimensional electroanatomic mapping system. Next, the n-quantity sites at which pacing was performed may be identified in the computational arrhythmia mapping system by analyzing each of the paced QRS complexes (e.g. the vectorcardiogram from the paced QRS complexes is computed) and comparing with the library of simulated pacing vectorcardiograms. The vectorcardiogram with the highest similarity score would provide information regarding the location of the site of pacing for that heartbeat. Next, the computational model and electroanatomic mapping system geometry are combined, either by export of the electroanatomic mapping geometry into the computational model arrhythmia mapping system, export of the computational model geometry into the electroanatomic mapping system, or conceptually, by using, for example, a least-squares fitting algorithm to best superimpose the positions of the n-quantity pacing locations. Ablation may then be performed at the source of the cardiac arrhythmia, as referenced to the position of the n-quantity of pacing sites identified in the computational simulation of the patient's anatomy.

<FIG> depicts an example of a computational model <NUM> with the location of a source of ventricular fibrillation shown, in accordance with some example embodiments. The computational simulation <NUM> shown in <FIG> may be a "heat map" indicating a location of a source of cardiac arrhythmia in a patient. The example of the computational model and mapping solution <NUM> shown in <FIG> may be generated, for example, from the clinical <NUM>-lead electrocardiogram (ECG) data of the arrhythmia of interest and its computed vectorcardiogram, matched to the simulated vectorcardiogram library of arrhythmia simulations. Although the computational model <NUM> shows the location of the source of cardiac arrhythmia, a precise relationship between the patient's anatomy and the geometry of the computational simulation <NUM> may be lacking. As such, the computational model and mapping solution <NUM> alone may not provide sufficient actionable data to a clinician treating the patient for cardiac arrhythmia.

In order to provide a precise location of the source of cardiac arrhythmia relative to the patient's anatomy, the data controller <NUM> is configured to align the computational simulation of a patient's anatomy, such as computational model and mapping output <NUM> shown in <FIG>, is aligned with an electroanatomic mapping <NUM> shown in <FIG>. Referring to <FIG>, the electroanatomic mapping <NUM> may track a position of one or more catheters (indicated by the top arrows) or the pacing electrodes of the pacemaker or implantable cardioverter-defibrillator (ICD) relative to the patient's anatomy (e.g., the left ventricle) as well as areas of low voltage (indicated by the bottom arrows). As such, in some example embodiment, the data controller <NUM> determines, based at least on an electroanatomic mapping, the location of an n-quantity (e.g., three or more) of pacing sites at which a catheter is positioned or pacing electrodes of a pacemaker or implantable cardioverter-defibrillator are located when applying one or more pacing impulses. To further illustrate, <FIG> depicts an example of an electroanatomic map <NUM> including pacing sites (indicated by the arrows). The data controller <NUM> further determines, in the computational mapping solution <NUM>, the location of the same n-quantity of pacing sites. <FIG> shows the computational model <NUM> in which the n-quantity of pacing sites are indicated by small white dots (noted by arrows). Notably, as shown in <FIG>, the location of the source of cardiac arrhythmia may be referenced relative to the location of the n-quantity of pacing sites. As described above, the computational model geometry is aligned with the <NUM>-dimensional electroanatomic mapping system geometry (or vice-versa) using a <NUM>-dimensional least-squares fitting algorithm referencing the locations of the n-quantity pacing locations. As a result of this alignment process, an updated <FIG> could be generated and displayed to the user, and targeted therapy may be more precisely delivered to the site of interest from <FIG> (labelled "source of cardiac arrhythmia").

The location of the source of cardiac arrhythmia is translated to the electroanatomic mapping system using a prolate spheroidal coordinate system that serves as a reference system for cardiac chambers. <FIG> depicts a prolate spheroidal coordinate system <NUM>, in accordance with some example embodiments. As shown in <FIG>, a location within the prolate spheroidal coordinate system <NUM> may be expressed as the tuple σ, τ, and φ, wherein σ = cosh (µ) and τ = cos (v).

Once the locations of the n-quantity of pacing sites are known in the computational simulation <NUM> and the electroanatomic map <NUM>, the positions of the n-quantity of pacing sites are used to align, using a transformative matrix A, the respective reference coordinate systems of the computational simulation <NUM> and the electroanatomic map <NUM>. The location of the source of the cardiac arrhythmia is further defined based on the locations of the n-quantity of pacing sites and is plotted, in the prolate spheroidal coordinate system <NUM>, with the tuple σsource, τsource, and φsource. The position of the source of cardiac arrhythmia relative to the n-quantity of pacing sites may be actionable data to a clinician treating the patient for cardiac arrhythmia. For example, the computational model from <FIG> may be aligned with electroanatomic mapping geometry from <FIG> using a least-squares fitting process. The geometry to be fitted could be transformed to the reference geometry via a process combining rotation, scaling, and translation (e.g. within the prolate spheroidal coordinate system, for example). A new image of the combined and aligned data (an "updated" <FIG>) could be generated and displayed to the user to allow precise targeting of the arrhythmia source. In particular, treatments including, for example, ablation, targeted gene therapy, stereotactic ablative radiotherapy (e.g., gamma radiation, proton beam), and surgical intervention, may be performed at the locations identified as the source of cardiac arrhythmia. For example, <FIG> depicts left ventricle and right ventricle geometries with multiple ablation sites where radiofrequency, cryogenic temperatures, ultrasound, and/or stereotactic ablative radiotherapy may be applied to alleviate the cardiac arrhythmia by disrupting and/or eliminating the erratic electric signals causing the dyssynchronous heart contractions associated with cardiac arrhythmia. <FIG> depicts an example of the delivery of stereotactic ablative radiotherapy (SAbR) in a patient with refractory ventricular arrhythmias.

<FIG> depicts a flowchart illustrating an example of a process <NUM> for enhancing a library of computational simulations with data associated with clinical cases, in accordance with some example embodiments. Referring to <FIG>, <FIG>, and <FIG>, the process <NUM> may be performed by the data controller <NUM> in order to supplement one or more of the computational simulations included in the library <NUM> with clinical data associated with clinical cases.

At <NUM>, the data controller <NUM> may receive, from the first user 145a, clinical data associated with a clinical case. For example, the data controller <NUM> may receive, from the first user 145a at the first client 140a, clinical data associated with a clinical case that includes, for example, patient anatomic information, diagnostic and/or treatment modalities, treatment parameters, treatment outcome, relevant medical literature, and/or the like.

At <NUM>, the data controller <NUM> may store, in the library <NUM>, at least a portion of the clinical data including by associated with the clinical data with a computational simulation having characteristics that most closely match the characteristics of the clinical case. For example, upon receiving the clinical data associated with the clinical case, the data controller <NUM> may compute, for each computational simulation in the library <NUM>, a similarity score indicative of a closeness of a match between the respective characteristics of the clinical case and the computational simulations in the library <NUM>. The clinical data associated with the clinical case may be indexed based on the computational simulation having a highest similarity score. For instance, the clinical data associated with the clinical case may be associated with the computational simulation whose characteristics (e.g., heart size, shape, presence or absence of structural heart disease, arrhythmia type, and/or the like) most closely match those in the clinical case.

At <NUM>, the data controller <NUM> may respond to a query from the second user 145b by at least sending, to the second user 145b, data from the library <NUM> including at least a portion of the clinical data associated with the clinical case. For example, the second user 145b may be treating a patient for cardiac arrhythmia and may thus query the library <NUM> based on patient data including, for example, patient age, medical history, proposed treatment plan, and/or the like. The data controller <NUM> may respond to the query from the second user 145b by at least identifying one or more clinical cases included in the library <NUM> that match the parameters of the query from the second user 145b. Alternatively and/or additionally, instead of one or more specific clinical cases, the second user 145b may apply a collection of clinical cases from the library <NUM> as training data to train a machine learning model to perform a variety of cognitive tasks including, for example, determining the statistical probability of the arrhythmia source location, performing a probabilistic analysis of potential clinical outcomes associated with different treatment approaches (e.g. ablation lesion location, number, and pattern) for arrhythmia, and/or the like.

In some example embodiments, the machine learning model may include a neural network such as, for example, an autoencoder and/or the like. The machine learning model may be trained based on training data that includes clinical data from a large number of patient cases which may be collected and entered as input into the machine learning model. Training data may include patient demographic information, electrocardiographic (ECG) and vectorcardiographic (VCG) tracings, and ground truth labels including the identified arrhythmia source locations. Arrhythmia source locations may be further labeled with ablation site, size, technique, internal targeting volume (ITV), planning targeting volume (PTV), ablation energy dose, and some ranking of the outcome (e.g. arrhythmia termination, acute ablation success, long-term ablation success, etc.). Moreover, the machine learning model may be trained to examine features present in the treatment approach for each patient (ablation lesion, number, size, configuration, internal targeting volume (ITV), planning targeting volume (PTV), ablation energy dose, etc.). Additionally, the machine learning model may determine a similarity metric between different clinical cases based on demographics, arrhythmia type, cardiac anatomy, etc. to determine relevance to both other training case data and/or future cases for comparison. When a user wants to utilize the trained machine learning model, the user may provide, as inputs to the trained machine learning model, a patient's electrocardiogram (ECG) or vectorcardiogram (VCG) as well as one or more patient characteristics and arrhythmia characteristics. The trained machine learning model may determine, based at least on the inputs, a statistical probability of the arrhythmia source location, and a probabilistic analysis of potential clinical outcomes associated with different treatment approaches (e.g. ablation lesion location, number, and pattern) for arrhythmia. Accordingly, the data controller <NUM> may also be configured to identify, based at least on the output of the trained machine learning model, a selection of relevant clinical cases for case reference and procedural planning.

<FIG> depicts a flowchart illustrating an example of a process <NUM> for modifying a library of computational simulations, in accordance with some example embodiments. Referring to <FIG>, <FIG>, <FIG>, and <FIG>, the process <NUM> may be performed by the data controller <NUM> in order to modify one or more computational simulations in the library <NUM> to better conform to patient specific characteristics.

At <NUM>, the data controller <NUM> may receive patient data collected during an electrophysiology study in either the electrophysiology laboratory, radiation medicine suite, or operating room. In some example embodiments, the data controller <NUM> may receive patient data collected during an electrophysiology (EP) study including, for example, action potential duration (APD) restitution data, conduction velocity restitution data, patient anatomical geometry, voltage mapping, intracardiac ultrasound data, patient demographics, cardiac activation pattern, regional conduction velocity, electrogram analysis, and/or the like.

At <NUM>, the data controller <NUM> may modify, based at least on the patient data, one or more computational simulations included in the library <NUM>. In some example embodiments, the data controller <NUM> may modify, based at least on the patient data, one or more of the non-patient specific computational simulations included in the library <NUM> such that the one or more non-patient specific computational simulations better conform to patient specific characteristics. These modifications may be performed in real time (or near real time) such that the modified library <NUM> of computational simulations may be available when the patient is treated for cardiac arrhythmia.

At <NUM>, the data controller <NUM> may send, to the first client 140a and/or the second client 140b, the modified computational simulations to enable the first user 145a and/or the second user 145b to determine, based at least on the modified computational simulations, a location of a source of cardiac arrhythmia and perform one or more treatments at the location of the source of cardiac arrhythmia. For example, the first user 145a and/or the second user 145b may perform, based at least on the modified computational simulations, a higher fidelity localization of the source of cardiac arrhythmia. Accordingly, the outcome of subsequent treatments performed at the source of cardiac arrhythmia may be improved due to the higher fidelity localization of the source of cardiac arrhythmia.

<FIG> depicts a flowchart illustrating an example of a process <NUM> for aligning a computational simulation with an electroanatomic mapping, in accordance with some example embodiments. Referring to <FIG>, <FIG>, <FIG>, <FIG>, and <FIG>, the process <NUM> may be performed by the data controller <NUM> in order to further localize the source of cardiac arrhythmia.

At <NUM>, the data controller <NUM> may identify, in an electroanatomic map, the location of an n-quantity of pacing sites at which a catheter is positioned when applying one or more pacing impulses. For example, as shown in <FIG>, the data controller <NUM> may identify, in the electroanatomic map <NUM>, one or more pacing sites.

At <NUM>, the data controller <NUM> may identify, in a computational simulation of a patient's anatomy, the location of the n-quantity of pacing sites. For instance, as shown in <FIG>, the data controller <NUM> may further identify, in the computational simulation <NUM>, the location of the same n-quantity of pacing sites.

At <NUM>, the data controller <NUM> may align, based at least on the location of the n-quantity of pacing sites, the electroanatomic map and the computational simulation of the patient's anatomy such that the location the source of cardiac arrhythmia is indicated by the location of the n-quantity of pacing sites. In some example embodiments, the data controller <NUM> may align the electroanatomic map <NUM> and the computational simulation <NUM> based on the location of the n-quantity of pacing sites. For example, once the locations of the n-quantity of pacing sites are known in the computational simulation <NUM> and the electroanatomic map <NUM>, the positions of the n-quantity of pacing sites may be used to align, using a transformative matrix A, the respective reference coordinate systems of the computational simulation <NUM> and the electroanatomic map <NUM>. Using, for example, a least-squares fitting algorithm incorporation rotation, translation, and scaling, alignment of the electroanatomic map <NUM> and the computational simulation <NUM> may be accomplished. Thus, the location of the source of cardiac arrhythmia may be further defined based on the locations of the n-quantity of pacing sites.

At <NUM>, the data controller <NUM> may generate a user interface displaying the location the source of cardiac arrhythmia relative to the location of the n-quantity of pacing sites. As noted, the position of the source of cardiac arrhythmia relative to the n-quantity of pacing sites may be actionable data to a clinician treating the patient for cardiac arrhythmia. Accordingly, the data controller <NUM> may provide this information to the first user 145a and/or the second user 145b including, for example, by generating a user interface displaying location the source of cardiac arrhythmia relative to the location of the n-quantity of pacing sites. Treatments including, for example, ablation, targeted gene therapy, stereotactic ablative radiotherapy (e.g., gamma radiation, proton beam), and surgical intervention, may be performed at the locations identified as the source of the cardiac arrhythmia. For example, as shown in <FIG>, the first user 145a and/or the second user 145b may perform treatments at the location of the source of the cardiac arrhythmia to alleviate the arrhythmia by disrupting and/or eliminating the erratic electric signals causing the abnormal heart contractions associated with arrhythmia.

<FIG> depicts a block diagram illustrating a computing system <NUM>, in accordance with some example embodiments. Referring to <FIG> and <FIG>, the computing system <NUM> can be used to implement the data controller <NUM> and/or any components therein.

As shown in <FIG>, the computing system <NUM> can include a processor <NUM>, a memory <NUM>, a storage device <NUM>, and input/output device <NUM>. The processor <NUM>, the memory <NUM>, the storage device <NUM>, and the input/output device <NUM> can be interconnected via a system bus <NUM>. The processor <NUM> is capable of processing instructions for execution within the computing system <NUM>. Such executed instructions can implement one or more components of, for example, the data controller <NUM>. In some implementations of the current subject matter, the processor <NUM> can be a single-threaded processor. Alternately, the processor <NUM> can be a multi-threaded processor. The processor <NUM> is capable of processing instructions stored in the memory <NUM> and/or on the storage device <NUM> to display graphical information for a user interface provided via the input/output device <NUM>.

The memory <NUM> is a computer readable medium such as volatile or nonvolatile that stores information within the computing system <NUM>. The memory <NUM> can store data structures representing configuration object databases, for example. The storage device <NUM> is capable of providing persistent storage for the computing system <NUM>. The storage device <NUM> can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input/output device <NUM> provides input/output operations for the computing system <NUM>. In some implementations of the current subject matter, the input/output device <NUM> includes a keyboard and/or pointing device. In various implementations, the input/output device <NUM> includes a display unit for displaying graphical user interfaces.

According to some implementations of the current subject matter, the input/output device <NUM> can provide input/output operations for a network device. For example, the input/output device <NUM> can include Ethernet ports or other networking ports to communicate with one or more wired and/or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet).

In some implementations of the current subject matter, the computing system <NUM> can be used to execute various interactive computer software applications that can be used for organization, analysis and/or storage of data in various (e.g., tabular) format. Alternatively, the computing system <NUM> can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities (e.g., generating, managing, editing of spreadsheet documents, word processing documents, and/or any other objects, etc.), computing functionalities, communications functionalities, and/or the like. The applications can include various add-in functionalities or can be standalone computing products and/or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input/output device <NUM>. The user interface can be generated and presented to a user by the computing system <NUM> (e.g., on a computer screen monitor, etc.).

These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. The machine-readable medium can alternatively, or additionally, store such machine instructions in a transient manner, such as for example, as would a processor cache or other random-access memory associated with one or more physical processor cores.

Claim 1:
A system, comprising:
at least one processor (<NUM>); and
at least one memory (<NUM>) including program code which when executed by the at least one processor (<NUM>) provides operations comprising:
determining (<NUM>), in an electroanatomic map (<NUM>), a location of each of an n-quantity of pacing sites at which a catheter, a pacemaker lead, or an implantable cardioverter defibrillator lead is positioned when applying one or more pacing impulses;
identifying a computational model (<NUM>) of a patient's anatomy and arrhythmia simulation associated with a vectorcardiogram that matches a patient vectorcardiogram collected while pacing at each of the n-quantity of pacing sites and determining (<NUM>) the location of the one or more same n-quantity of pacing sites in the computational model (<NUM>), wherein the vectorcardiogram associated with the computational model is indexed to a source location of a cardiac arrhythmia;
aligning (<NUM>), based at least on the location of each of the n-quantity of pacing sites in the electroanatomic map (<NUM>) and the computational model (<NUM>) and using a three-dimensional fitting algorithm, a respective reference coordinate system of the electroanatomic map (<NUM>) and the computational model (<NUM>); and
generating (<NUM>), based at least on the aligning (<NUM>), an indication of a location of a source of a clinically relevant cardiac arrhythmia in the computational model (<NUM>) relative to the location of each of the n-quantity of pacing sites.