METHODS AND SYSTEMS FOR FACILITATING DIAGNOSING OF A CENTRAL OR PERIPHERAL VASCULATURE DISORDER USING INTRAVASCULAR IMAGING

Disclosed herein is a method of facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, the method may include a step of generating, using an intravascular imaging device, at least one intravascular image associated with a patient. Further, the method may include a step of analyzing, using a processing device, the at least one intravascular image. Further, the method may include a step of determining, using the processing device, at least one vein diagnosis based on the analyzing. Further, the method may include a step of displaying, using a display device, the at least one vein diagnosis. Further, the method may include a step of storing, using a storage device, the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database. In other embodiments, an artificial intelligence unit may be configured to reconstruct missing data in at least one intravascular image and determine a value associated with the at least one intravascular image.

FIELD OF THE INVENTION

Generally, the present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods and systems for facilitating a central or peripheral vasculature disorder using intravascular imaging.

BACKGROUND OF THE INVENTION

Vasculature disorders has been ignored by medical professionals. Further, upon labeling the vasculature disorders as low concern and hard to diagnose, vasculature disorder (or disease) cases may be untreated that may lead patients to serious health circumstances. Further, the vasculature disorders may include central vasculature disorders and peripheral vasculature disorders. Medical professionals may be slowly adopting the philosophy that vasculature disorders may be a problem worth intervening. Further, the vasculature disorders are only recently being taught as a concern at most medical schools. Further, the concerns for the vasculature disorders may be projected to grow exponentially with the increasing focus on the vasculature disorders.

Existing techniques for facilitating diagnosing of a vasculature disorder are deficient with regard to several aspects. For instance, current technologies diagnose the vasculature disorder by comparing cross-sectional area of a compressed vein with a standard reference. For, instance, the current technologies make use of a decision-making model that is deficient in measuring the cross-sectional area of the compressed vein. Further, the decision-making model is subjective, anatomic, and non-physiologic.

Further, in many instances, medical images, such as intravascular ultrasound (IVUS) images, may contain artifacts or missing data. There exists a need for an efficient, automated system to detect and repair missing data from IVUS images to improve the quality of the images to assist a professional in rendering a diagnosis. It is further desirable to be able to efficiently extract a value from intravascular images—such as a minimal cross sectional area of a vein.

Therefore, there is a need for improved methods and systems for facilitating diagnosing of a central or peripheral vasculature disorder using intravascular imaging that may overcome one or more of the above-mentioned problems and/or limitations.

SUMMARY OF THE INVENTION

Disclosed herein is a method of facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, the method may include a step of generating, using an intravascular imaging device, at least one intravascular image associated with a patient. Further, the method may include a step of analyzing, using a processing device, the at least one intravascular image. Further, the method may include a step of determining, using the processing device, at least one vein diagnosis based on the analyzing. Further, the method may include a step of displaying, using a display device, the at least one vein diagnosis. Further, the method may include a step of storing, using a storage device, the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database.

Further disclosed herein is a system for facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, the system may include an intravascular imaging device configured for generating at least one intravascular image associated with a patient. Further, the system may include a processing device communicatively coupled with the intravascular imaging device. Further, the processing device may be configured for analyzing the at least one intravascular image. Further, the processing device may be configured for determining at least one vein diagnosis based on the analyzing. Further, the system may include a display device communicatively coupled with the intravascular imaging device. Further, the display device may be configured for displaying the at least one vein diagnosis. Further, the system may include a storage device communicatively coupled with the processing device. Further, the storage device may be configured for storing the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database.

Further disclosed is the inclusion of an artificial intelligence unit for facilitating accurate and efficient processing of any intravascular images. The artificial intelligence unit may be adapted to identify missing data, repair missing data, determine a value from a medical image, identify confluences, and determine at least one vein diagnosis based on the medical image.

DETAILED DESCRIPTION

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of methods and systems for facilitating diagnosing of a central or peripheral vasculature disorder using intravascular imaging, embodiments of the present disclosure are not limited to use only in this context. It should be understood that, for the purposes of the written description, any reference to “intravascular imaging” or “IVUS images” or “IVUS frames” should be construed to include any similar medical imaging, including but not limited to: MRI imaging, x-ray imaging, ultrasound imaging generally, or any other similar medical procedures for producing visual images that are well-known in the art. It should be further understood that any reference to a “frame” should be construed to mean one image selected from one or more images from the same set of medical images. As a non-binding example, an IVUS procedure taken over three seconds may produce 90 frames of data at a rate of 30 frames per second, each frame corresponding to a single image taken from within that IVUS procedure.

The present disclosure may describe methods and systems to facilitate diagnosing of a vasculature disorder using intravascular imaging. Further, the vasculature disorder may include a central vasculature disorder and a peripheral vasculature disorder. Further, the present disclosure may describe methods and systems for quantifying venous flow changes using intravascular images. Further, intravascular images may include intravascular ultrasound (IVUS) images. Further, the present disclosure may be designated for the treatment of Pelvic Venous Compression (PVC) cases associated with the vasculature disorder. Further, pelvic congestion syndrome (PCS) associated with the Pelvic Venous Compression (PVC) was brought into the spotlight with the identification of May-Thurner Syndrome. Further, the May-Thurner syndrome is a rarely diagnosed condition in which patients may develop deep venous thrombosis (DVT) due to an anatomical variant in which the right common iliac artery overlies and compresses the left common iliac vein against the lumbar spine. Further, the anatomical variant may be present in over 20% of the population. Further, until recent advancements with Intravascular Ultrasound (IVUS), PCS has been extremely difficult to diagnose and treat. Couple this with the inexperience of vein surgeons treating venous disease; it has left surgeons with a completely subjective viewpoint of treating the PCS disease. Further, the disclosed methods and systems may accomplish a plurality of goals. Further, the plurality of goals may include identification of the prevalence of the disease, generation/creation of an objective standard for when to intervene, fortification of the importance of venous disease diagnosis, and laying credible evidence to bring in late adopters (expand the market). Further, the venous disease/disorder is the inadequate function of the vein (weakened or defective valves) or the inadequate flow of blood through the vein (blockages or venous compressions). Venous Disease can be broken down into three main categories: Venous Insufficiency (VI), Venous Thromboembolic (DVT/PE), and Pelvic Venous Compression (Iliac vein compression syndrome, pelvic venous congestion—PCS). Venous Insufficiency is the most common form of Venous Disease and is usually superficial and treatable. Venous Thromboembolic conditions such as DVT are the most urgent. Until recent advancements of imaging devices, Pelvic Venous Compression has been rarely considered. According to Thomas Wright, M.D., FACP, RVT, Medical Director of Laser Lip & Vein Center in St. Louis, MO, “Untreated venous issues can lead to a multitude of serious health problems, including variceal bleeding, venous ulcers, and blood clots, also known as deep venous thrombosis. It's important to dispel the myth that Venous Insufficiency is just a cosmetic issue. Leaving venous issues untreated can eventually lead to much larger problems.” Further, it may be a surprise to know that venous disease treatment is not a specialty taught in medical school. In the mid-1990s vein specialists taught themselves and then each other. Further, with the lack of awareness that many specialists may have not acknowledged that a venous disease may be a serious health concern. According to Deepak Sudheendra, MD, FSIR, RPVI, Vascular Interventional Radiologist, “As a physician, I can honestly say that I did not learn anything about Venous Disease in medical school. It was not discussed! Imagine every medical student in the country going off to practice medicine with little to no knowledge of Venous Disease!”

Further, the present disclosure may describe methods and systems to facilitate the detection and quantification of the severity of venous disease from Intravascular Ultrasound (IVUS) images. Further, upon using proprietary algorithms and calculations, the disclosed system may filter raw data received by the IVUS machine and convert the raw data into an objective evaluation that may identify the need for whether or not to intervene. Further, the disclosed methods and system mays fortify the importance of the Venous Disease diagnosis with quantifiable patient outcome reports that may be collected using the above mentioned objectified standards. Further, with the efforts of the previously laid out goals, our product will then be able to lay credible evidence to bring in late adopters (medical specialists) and expand the market.

Further, the disclosed methods and systems may relate to treating venous diseases, and more particularly to cases concerning Pelvic Venous Compression. Further, treating Pelvic Venous Compression currently depends on subjective decision making. Further, without a standard for interpreting the images obtained using IVUS; this subjective decision-making may lead to various interpretations regarding the severity of the disease, and more specifically whether or not to intervene and to what extent of intervention.

Further, the present disclosure may describe methods and systems that may solve the problem of interpreting the images by providing a quantifiable and qualitative physiological analysis of the patient's anatomical conditions including the identification and isolation of the problem (blockage, compression, lesion, thrombosis), and providing the surgeon with therapeutic decision making assistance.

According to some embodiments, the disclosed methods and systems enables setting a standard for intervention, an objective means of interpreting the IVUS image, increased confidence for intervention, a more efficient treatment strategy for the patient, and is a tool that will be used for the further evaluation and research of varying venous/cardiovascular diseases.

Further, the disclosed methods and systems may be utilized by interventionalists, surgeons, community and university hospitals, stent manufacturers, insurance companies, researchers, statisticians, and so on. Further, the surgeons may be the obvious users of this product. The initial intent is the use within the operating room as a therapeutic decision-making device designed to aid the surgeon with an objective reasoning of whether or not to intervene. Further, the second market may be hospitals. Hospitals may benefit from the use of the product as a result of better logging of treatment efficacy. Further, the disclosed methods and systems may lessen the likelihood of return patients. Further, the third market may be universities. Further, the universities may perform additional studies that may further strengthen evidence of the prevalence of this disease. Further, the fourth market may be stent manufacturers. Further, through efforts of objectified diagnosis, and improved patient outcomes, late adopters may broaden the market of this procedure and as a result—increase the need for the use of stents. Further, the fifth and sixth markets may be Insurance companies and researchers & statisticians. Further, the disclosed methods and systems may set up a nationwide (to be global) database of case studies. Further, documenting the evidence of a need for intervention, and identifying a prevalent demographic may result in better patient care.

Further, over 30 million Americans may be suffering from venous diseases and only 10 percent seek treatment, according to society for Vascular Medicine. Further, according to the Vascular Disease Foundation, a large U.S. survey, the Framingham study, reported that 27 percent of the American adult population had some form of venous disease in their legs. Further, through the efforts and the education of late adopters to venous diseases, patients suffering from venous diseases may greatly rise. Further, a plurality of medical specialists may accept the prevalence of the patients suffering from forms of venous disease. Further, the plurality of medical specialists may include orthopedics, dermatology, obstetrics and gynecology, wounds care doctors, urologists, neurology/sleep doctors, and so on. Further, the forms of venous disease may include ortho venous disease, dermato venous disease, Pelvic Congestion Syndrome (PCS), venous origin ulcer, night-time urination, Restless Leg Syndrome (RLS) and leg cramps, and so on. According to the Society for Vascular Surgery, chronic venous diseases may affect up to 40% of the U.S population. This percentage of the population may refer to the number of patients with chronic venous disease going untreated. Further, the disclosed methods and systems may begin to attract the late adopters and increase the number of diagnosed patients with any form of chronic venous disease to an estimated 130,280,000.

Further, the disclosed system may use existing anatomic data recorded from Ultrasound Images to calculate physiologic data and detect a presence of venous compression that may be used to make objective clinical decisions regarding whether to intervene and to what degree. Further, the present disclosure may describe methods and systems that may include automated lumen measurements, proprietary image filtering methods, statistical and probability analysis for diagnosis and treatment. Further, the disclosed system may be handled as a “black box” attachment to the main imaging machine.

Further, the disclosed methods and systems may facilitate the expression of iliac vein compression lesions in terms of physiologic flow reduction. Further, the disclosed methods and systems may facilitate expression of Iliac vein stent results in terms of physiologic flow improvement. Further, the disclosed methods and systems may assist the operator in deciding when to intervene to determine how much improvement. Further, the disclosed methods and systems may assist the operator in deciding when to intervene to determine if a need to intervene further, or what future steps to take in the patient's treatment. Further, the disclosed methods and systems may create an objective standard of when to intervene.

According to some embodiments, the disclosed methods and systems may facilitate the identification of prevalence of Pelvic Venous Compression disease and fortify the importance of venous disease diagnosis. Further, the disclosed methods and systems may facilitate establishment of credible evidence for the necessity of the procedure versus a non Intravascular Ultrasound (IVUS) procedure. Further, the disclosed methods and systems may begin with raw data collected from the Intravascular Ultrasound (IVUS) machine. Further, the raw data may be filtered using proprietary image filtering methods to provide a controlled version of the data. Further, the data may be analyzed using algorithms and calculations. Further, a therapeutic conclusion is generated to aid in the surgeon's decision making. Further, the data may be stored in a database for future analysis, in an effort to increase understanding of the condition and provide better patient outcomes.

Further, the image interpretation process may take place in two phases. Further, the two phases may include raw image manipulation and data analysis. Further, the raw image manipulation may initiate filtering process, upon uploading the images to the disclosed system. Further, the filtering process may include adjusting brightness, contrast, etc. to identify the veins (common iliac, external iliac, common femoral, etc.) and to detect lumen border. Further, the filtering process may include artificial intelligence analysis for automated detection of the lumen border associated with the veins. Further, the filtering process may include convolutional neural network implementation for automated detection of the lumen border associated with the veins. Further, the filtering process may include simultaneous filtering analysis to determine the best fit line to facilitate lumen area measurements, 3D modeling of veins, identifying maximum and minimum area sites of veins, identifying image slides where these sites occur. Further, the raw image manipulation process may provide information that may be required for data analysis. Further, the data analysis may include analysis of change in blood flow and change in area. Further, the data analysis may include therapeutic decision making that may include recording patient's metrics (such as height, weight, age, area measurements, etc.) and comparing the metrics to the database of patients. Further, the database may find “similar” patients (metrics) with their known outcomes (Patient Outcome Surveys & Any Other Patient Outcome Analysis) and compares them to current patients to perform a confidence analysis. Further, the confidence analysis may generate a numerical value that will allow to perform a second logic test [a numerical test that may set conditional boundaries and appropriate responses to each range of numerical values, Ex: 0<0.25 (intervene), 0.25≤0.5 (Alternative Treatment), 0.5≤0.75 (Monitor), 0.75<1 (Clear)] that may generate a “Best Fit” outcome in the form of what diagnostic steps to take. Further, the therapeutic decision making may include a second data analysis that may be required to perform.

Further, the present disclosure may describe methods and systems that may facilitate treatment of Pelvic Venous Compressions, and specifically May-Thurner Syndrome. Further, May-Thurner Syndrome is a rarely diagnosed condition in which patients may develop deep venous thrombosis (DVT) due to an anatomical variant in which the right common iliac artery overlies and compresses the left common iliac vein against the lumbar spine. further, the treatment of Pelvic Venous Compression disorders may be facilitated by analyzing data through confidence analysis, outlier filtering methods, relative max and min analysis, therapeutic decision making factors, patient database analysis.

Further, the use of artificial intelligence and analysis methods are effective in reducing the number of frames from each set of intravascular images that must be analyzed to effectively determine a value, such as a confluence, cross sectional area, or similar relevant value. In the ideal embodiment, it has been found that the analysis methods and implementation described herein reduce the amount of IVUS frames that must be analyzed to output some analyzed value with confidence is reduce by 5-10% of the total frames that would otherwise need analysis by conventional methods.

FIG.1is an illustration of an online platform100consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform100to facilitate diagnosing of a central or peripheral vasculature disorder using intravascular imaging may be hosted on a centralized server102, such as, for example, a cloud computing service. The centralized server102may communicate with other network entities, such as, for example, a mobile device106(such as a smartphone, a laptop, a tablet computer etc.), other electronic devices110(such as desktop computers, server computers etc.), databases114, and sensors116over a communication network104, such as, but not limited to, the Internet. Further, users of the online platform100may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

A user112, such as the one or more relevant parties, may access online platform100through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device900.

FIG.2is a block diagram of a system200configured for facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, the system200may include an intravascular imaging device202, a processing device204, a display device206and a storage device208.

Further, the intravascular imaging device202may be configured for generating at least one intravascular image associated with a patient.

Further, the processing device204may be communicatively coupled with the intravascular imaging device202. Further, the processing device204may be configured analyzing the at least one intravascular image. Further, the processing device204may be configured for determining at least one vein diagnosis based on the analyzing.

Further, the display device206may be communicatively coupled with the intravascular imaging device202. Further, the display device206may be configured for displaying the at least one vein diagnosis.

Further, the storage device208may be communicatively coupled with the processing device204. Further, the storage device208may be configured for storing the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database.

In further embodiments, a communication device may be communicatively coupled with the processing device204. Further, the communication device may be configured for receiving patient metric data associated with the patient from at least one external device. Further, the processing device204may be configured for analyzing the patient metric data. Further determining of the at least one vein diagnosis may be further based on the analyzing of the patient metric data. Patient metric data may include any physiological data, intervention history, demographic information, and venous measurements available in the patient's medical history. Once received by the processing device204, the patient metric data may be stored in the storage device208. Patient metric data may later be accessed by the processing unit or artificial intelligence unit for further analysis and processing.

Further, in some embodiments, the at least one vein diagnosis may be associated with at least one vasculature disorder. Further, the at least one vasculature disorder may include a central vasculature disorder and a peripheral vasculature disorder. Further, the at least one vasculature disorder may be associated with at least one of a blockage, a compression, a lesion and a thrombosis of at least one vein.

Further, in some embodiments, the processing device204may be configured for identifying at least one vein associated with at least one vasculature disorder based on the analyzing. Further, the processing device204may be configured for detecting a lumen border associated with the at least one vein. Further, the processing device204may be configured for generating at least one 3D vein model corresponding to the at least one vein based on the detecting. Further, the at least one 3D vein model may be associated with a cross-sectional area and a measure of fluid flow. Further, the determining of the at least one vein diagnosis may be based on the at least one 3D vein model.

Further, in some embodiments, the processing device204may be further configured for generating at least one intervention indication based on the at least one vein diagnosis. Further, the display device may be configured for displaying the at least one intervention indication.

Further, in some embodiments, the at least one intervention indication may be associated with an improvement of fluid flow in at least one vein. Further, the improvement of fluid flow in the at least one vein relates to recovering of the at least one vein from at least one vasculature disorder.

Further, in some embodiments, the at least one intervention indication may include a plurality of options. Further, each option of the plurality of options corresponds to a treatment approach for the patient.

In further embodiments, at least one biological sensor may be communicatively coupled with the processing device204. Further, the at least one biological sensor may be configured for generating at least one patient data associated with the patient. Further, the processing device204may be configured for analyzing the at least one patient data. Further, the determining of the at least one vein diagnosis may be further based on the analyzing of the at least one patient data.

Further, in some embodiments, the processing device204may be further configured for identifying at least one vasculature disorder based on the least one intravascular image. Further, the storage device may be further configured for retrieving at least one historical patient data based on the identifying. Further, the processing device may be configured for analyzing the at least one historical patient data. Further, the determining of the at least one vein diagnosis may be further based on the analyzing of the at least one historical patient data.

In further embodiments, at least one therapy device may be communicatively coupled with the intravascular imaging device202. Further, the at least one therapy device may be configured to provide at least one therapy to the patient based on the at least one vein diagnosis.

FIG.3is a flowchart of a method300for facilitating diagnosing of a vasculature disorder using intravascular imaging, in accordance with some embodiments. Accordingly, at302, the method300may include a step of generating, using an intravascular imaging device (such as the intravascular imaging device202), at least one intravascular image associated with a patient.

Further, at304, the method300may include a step of analyzing, using a processing device (such as the processing device204), the at least one intravascular image.

Further, at306, the method300may include a step of determining, using the processing device, at least one vein diagnosis based on the analyzing. Further, the at least one vein diagnosis may be associated with at least one vasculature disorder. Further, the at least one vasculature disorder may include a central vasculature disorder and a peripheral vasculature disorder. Further, the at least one vasculature disorder may be associated with at least one of a blockage, a compression, a lesion and a thrombosis of at least one vein.

Further, at308, the method300may include a step of displaying, using a display device (such as the display device206), the at least one vein diagnosis.

Further, at310, the method300may include a step of storing, using a storage device (such as the storage device208), the at least one vein diagnosis and the at least one intravascular image associated with the at least one vein diagnosis in a database.

In further embodiments, the method300may include a step of transmitting, using a communication device, at least one vein diagnosis to at least one therapy device communicatively coupled with the intravascular imaging device. Further, the at least one therapy device may be configured to provide at least one therapy to the patient based on the at least one vein diagnosis.

FIG.4is a flowchart of a method400for facilitating determination of vein diagnosis based on patient metric data, in accordance with some embodiments. Accordingly, at402, the method400may include a step of receiving, using a communication device, patient metric data associated with the patient from at least one external device.

Further, at404, the method400may include a step of analyzing, using the processing device, the patient metric data. Further, the determining of the at least one vein diagnosis may be further based on the analyzing of the patient metric data.

FIG.5is a flowchart of a method500for facilitating the generation of a 3D vein model corresponding to a vein, in accordance with some embodiments. Accordingly, at502, the method500may include a step of identifying, using the processing device, at least one vein associated with at least one vasculature disorder based on the analyzing. Further, the at least one vasculature disorder may include a central vasculature disorder and a peripheral vasculature disorder.

Further, at504, the method500may include a step of detecting, using the processing device, a lumen border associated with the at least one vein. Further, the detecting may include artificial intelligence analysis and convolutional neural network implementation.

Further, at506, the method500may include a step of generating, using the processing device, at least one 3D vein model corresponding to the at least one vein based on the detecting. Further, the at least one 3D vein model may be associated with a cross-sectional area and a measure of fluid flow. Further, the determining of the at least one vein diagnosis may be based on the at least one 3D vein model.

FIG.6is a flowchart of a method600for facilitating generation and displaying of intervention indication, in accordance with some embodiments. Accordingly, at602, the method600may include a step of generating, using the processing device, at least one intervention indication based on the at least one vein diagnosis. Further, the at least one intervention indication may be associated with an improvement of fluid flow in at least one vein. Further, the improvement of fluid flow in the at least one vein relates to recovering of the at least one vein from at least one vasculature disorder. Further, the at least one vasculature disorder may include a central vasculature disorder and a peripheral vasculature disorder. Further, the at least one intervention indication may include a plurality of options. Further, each option of the plurality of options corresponds to a treatment approach for the patient

Further, at604, the method600may include a step of displaying, using the display device, the at least one intervention indication.

FIG.7is a flowchart of a method700for facilitating determination of vein diagnosis based on patient data, in accordance with some embodiments. Accordingly, at702, the method700may include a step of receiving, using a communication device, at least one patient data associated with the patient from at least one biological sensor. Further, the at least one biological sensor may be configured to generate the at least one patient data.

Further, at704, the method700may include a step of analyzing, using the processing device, the at least one patient data. Further, the determining of the at least one vein diagnosis may be further based on the analyzing of the at least one patient data.

FIG.8is a flowchart of a method800for facilitating determination of vein diagnosis based on historical patient data, in accordance with some embodiments. Accordingly, at802, the method800may include a step of identifying, using the processing device, at least one vasculature disorder based on the at least one intravascular image.

Further, at804, the method800may include a step of retrieving, using the storage device, at least one historical patient data based on the identifying.

Further, at806, the method800may include a step of analyzing, using the processing device, the at least one historical patient data. Further, the determining of the at least one vein diagnosis may be further based on the analyzing of the at least one historical patient data.

With reference toFIG.9, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device900. In a basic configuration, computing device900may include at least one processing unit902and a system memory904. Depending on the configuration and type of computing device, system memory904may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory904may include operating system905, one or more programming modules906, and may include a program data907. Operating system905, for example, may be suitable for controlling computing device900's operation. In one embodiment, programming modules906may include image-processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG.9by those components within a dashed line908.

Computing device900may have additional features or functionality. For example, computing device900may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.9by a removable storage909and a non-removable storage910. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory904, removable storage909, and non-removable storage910are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device900. Any such computer storage media may be part of device900. Computing device900may also have input device(s)912such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s)914such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

As stated above, a number of program modules and data files may be stored in system memory904, including operating system905. While executing on processing unit902, programming modules906(e.g., application920such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit902may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

In some embodiments, the processing device204may further comprise an artificial intelligence unit. In other embodiments, the processing device204may be the artificial intelligence unit, and the artificial intelligence unit may perform any functions otherwise performed by the processing device204. The artificial intelligence unit may comprise any processing unit or computing device that implements any artificial intelligence technology as is known in the art, such as machine learning, a neural network, or other similar artificial intelligence technology.

The artificial intelligence unit may be configured for identifying at least one incomplete area in the at least one intravascular image. For example, a portion of the intravascular image may be obscured by an artifact such as a motion artifact, or a portion of the intravascular image may be missing due to an error during the ultrasound test. The artificial intelligence may be trained to identify the missing portion of the intravascular image. In some embodiments, one or more intravascular images that do not contain any artifacts or missing data may be identified by the processing unit stored in the storage unit or database, these intravascular images being hereinafter referred to as complete intravascular images. These complete intravascular images may then be used to train the artificial intelligence unit to better recognize an intravascular image that may have missing information or artifacts. These complete intravascular images may further be used to train the artificial intelligence unit to reconstruct an intravascular image to repair missing information or remove artifacts from the at least one intravascular image.

In some embodiments, the artificial intelligence unit may be configured to identify a confluence. The confluence may comprise an area where one or more veins connect or overlap. The artificial intelligence unit may be further configured for identifying a region of interest in a confluence of veins based on the at least one intravascular image. The artificial intelligence unit may first be adapted to identify a confluence of veins in one or more intravascular images. For example, the artificial intelligence unit may select at least one image from one or more intravascular image that meet the characteristics of a confluence. Next, the artificial intelligence unit may be adapted to identify a region of interest inside the confluence. A region of interest may be any area with medically significant information that may be desired by a physician or medical personnel, such as the lumen border, the cross sectional area of the lumen border, or other similar information regarding the subject of the intravascular image or other medical image.

The artificial intelligence unit may be further configured for measuring a physiological contribution of a plurality of veins involved in the confluence of veins. A physiological contribute may comprise blood flow, volume, characteristics of blood flow, or other similar data relating to the confluence of veins. In cases where a venous constriction occurs at a confluence, the decision to intervene may be subjective. By assessing the physiological contribution of the plurality of veins involved in the confluence of veins, the artificial intelligence unit may better assess, diagnose, or recommend a course of action or intervention for the patient.

In some embodiments, the artificial intelligence unit may be configured for processing a plurality of intravascular images, ideally to process or identify a value of interest, such as a cross-sectional area or lumen border. In the ideal embodiment, the artificial intelligence unit may first be configured to space each intravascular image by a fraction of the total length of the plurality of intravascular images to create a subset of the plurality of intravascular images. In the ideal embodiment, the artificial intelligence unit may select an image at each 10% of the way through the imaging process from the plurality of intravascular images, though other values are contemplated.

Next, the artificial intelligence unit is configured to analyze the subset of the plurality of intravascular images to create an analysis result. For example, the artificial intelligence unit may be configured to identify a cross sectional area, a lumen border, or any other value of interest for each intravascular image in the subset.

Once each image in the subset has been analyzed, the artificial intelligence unit may be further configured to recursively subdivide the analysis result until a final result is reached. For example, if the artificial intelligence unit is seeking the minimum cross sectional area, the artificial intelligence unit may identify one or more intervals in which a minimum cross sectional area may be present, and repeat the process of analyzing the subset of the interval. For example, if the artificial intelligence unit divides a plurality of intravascular images into ten intervals and identifies a potential minimum cross sectional area in the second interval, the artificial intelligence unit may then subdivide the second interval into another set of ten intervals. The artificial intelligence unit is then configured to repeat this process recursively until a condition is met. The condition may be any condition decided by the user such as identification of a value within a margin of error, or the process may be stopped after a certain number of iterations.

In other embodiments, the analyzing may comprise a search algorithm that is similar to a binary search. First, the analyzing comprises the step of selecting a start frame, a end frame, and a middle frame from a plurality of intravascular images. The middle frame is positioned between the start frame and the end frame, either temporally or physically. A first frame is then selected, the first frame being positioned between the start frame and the middle frame. A second frame is then selected, the second frame being positioned between the middle frame and the end frame. The artificial intelligence unit may then analyze the first frame and the second frame. The analysis may include analyzing each frame to extract a value, such as a cross sectional area, lumen border size, or other similar value or characteristic from the frame. Once the value is extracted, the values in first frame and the second frame are compared, and either the smaller or larger of the two values may be selected. Once selected, the first frame may become the new start frame, and the second frame may become the new end frame. A new middle frame is then generated between the first frame and the second frame, and the analysis repeats. This process repeats until a threshold value is reached. The threshold value may be a number of iterations, a determination that a minimum has been reached based on other factors considered by the artificial intelligence unit, or other condition decided upon by a user. In the ideal embodiment, the above process is used by the artificial intelligence unit to find a minimum cross sectional area from a plurality of IVUS images.

In the ideal embodiment to find a minimal cross sectional area, the start and end frames may be recursively selected as follows to continuously generate new subsets of frames to search. Each time a new start and end frame are selected, a new first and a new second frame are also selected between the new start and end frame as described above, until some threshold value is reached. Once a first frame and second frame are chosen, the values of the first frame and the second frame are compared. If the first frame has a smaller value than the second frame, and the start frame has a smaller value than the second frame: the start and end frame are reselected, with the start frame being the original start frame, and the end frame being the first frame. If the first frame has a smaller value than the second frame and the middle frame has a smaller value than the start frame: the first and second frame are reselected, with the new start frame being the first frame, and the new end frame being the middle frame. If the second frame has a smaller value than the first frame, and the middle frame has a smaller value than the final frame: the start and end frame are reselected, with the middle frame being the new start frame, and the second frame being the new end frame. If the second frame has a smaller value than the first frame, and the final frame has a smaller value than the middle frame: then the start and end frame are reselected, with the second frame being the new start frame, and the final frame being the new end frame. This process then continues, as a new subset of frames is generated by the above. A new first frame and a new second frame are selected from the new subset of frames, and the process repeats until some threshold value is reached. In the ideal embodiment, the process stops when only 10 total frames remain in the new subset. At this point, every remaining frame is searched to determine a minimum.

In some embodiments, the artificial intelligence unit may be configured to merge together a plurality of intravascular images. For example, the artificial intelligence unit may merge together images taken from multiple IVUS scans into a merged intravascular image. Once merged, the artificial intelligence unit may be further configured for merging together the plurality of intravascular images to establish a congregated average. For example, when calculating a cross sectional area, the artificial intelligence unit may be configured to take the mean of the minimum cross sectional area of a vein across the merged intravascular image. This process improves accuracy, especially when applied to the reconstruction of missing data or artifacts in an intravascular image, as described above.

In some embodiments, the database may be further configured for storing at least one intravascular image, the selected intravascular image being ideally selected as being free of artifacts and without any missing data. The database may be communicatively coupled to the artificial intelligence unit, such that the database is configured to repeatedly send any newly received intravascular images to the artificial intelligence unit for processing. Once received, the artificial intelligence unit may use the received intravascular images to train a machine learning model. By training on complete images without artifacts, the artificial intelligence unit may be continuously trained and updated to better reconstruct any intravascular images that have artifacts or are otherwise missing data. In the ideal embodiment, any time a plurality of intravascular images are received, any of those plurality of intravascular images that are free of artifacts or missing data are sent to the artificial intelligence unit to assist with training.

As seen inFIG.10, a method1000for repairing medical images is described. At1002, the method may comprise identifying, using the artificial intelligence unit, at least one incomplete area in the at least one intravascular image. At1004, the method may comprise reconstructing, using the artificial intelligence unit, the at least one incomplete area.

As seen inFIG.11, a method1100for assessing regions of interest is described. At1102, the method may comprise identifying, using the artificial intelligence unit, a confluence of veins based on the at least one intravascular image. At1104, the method may further comprise identifying, using the artificial intelligence unit, a region of interest for a confluence of veins. At1106, the method may further comprise measuring, using the artificial intelligence unit, a physiological contribution of a plurality of veins involved in the confluence of veins.

For the method1200, the at least one intravascular image comprising a plurality of intravascular images. At1202, the method may comprise spacing, using the artificial intelligence unit, each intravascular image in the plurality of intravascular images by a fraction of a total length of the plurality of intravascular images to create a subset of the plurality of intravascular images. At1204, the method may comprise analyzing, using the artificial intelligence unit, a subset of the plurality of intravascular images to create an analysis result. At1206, the method may comprise recursively subdividing, using the artificial intelligence unit, a plurality of intervals of the analysis result until a final result is reached.

As shown inFIG.13, the analyzing of1206is further described as method1300. The analyzing at1302may further comprise analyzing a first frame between a start frame and a middle frame. The method at1304may further comprise analyzing a second frame between the middle frame and a final frame. The method at1306may further comprise comparing the first frame and the second frame to determine which frame has a smaller cross section. The method at1306may further comprise repeating the above steps until a frame with a smallest cross section is found. For example,1306may check to see if a threshold value has been reached. If no threshold value is reached, the method may return to1302. It should be understood that the above method may be applied not just to determining a cross section or cross sectional area, but also to extracting any value which may be based on comparison.

A method of merging multiple intravascular images is described. The method may comprise merging, using the processing device, the plurality of intravascular images to establish a congregated average.

As shown inFIG.14, a method1400of training an artificial intelligence unit is described. The method at1402may comprise storing, using the storage device, the at least one intravascular image, wherein the at least one intravascular image selected for storage is free of artifacts. The method at1404may further comprise sending, using the storage device, the at least one intravascular image to the artificial intelligence unit. The method at1406may further comprise training a machine learning model, using the at least one intravascular image to train the machine learning model. Though a machine learning model is described, it should be understood that the above method may be applied to an similar artificial intelligence model, including but not limited to neural networks. In the ideal embodiment, it should be understood that method1400is a global process using a global database for storage, such that any IVUS procedure performed anywhere on the globe may be used for training. This configuration allows for a wide variety of IVUS procedures from varied individual backgrounds to be used to rapidly improve the accuracy and efficiency of the artificial intelligence unit.