# US 2026/0065484 A1 · Systems and methods for use of generative artificial intelligence (ai) in cardiac patient care > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0065484 A1 - Document ID: US20260065484A1 - Status: Published application - Kind: A1 - Application number: 19/320,787 - Filed: 2025-09-05 - Published: 2026-03-05 - Earliest claimed: 2024-09-05 - Assignees: HeartFlow, Inc. - Applicants: Heartflow, Inc. - Inventors: Michiel SCHAAP; Matthew SINCLAIR; Andreas SCHUH; Peter Kersten PETERSEN; Esther PUYOL ANTON; Samuel GERBER; Souma SENGUPTA; Benjamin Michael GLOCKER; Timothy A. FONTE; James BATTEN; Tian XIA; Nan XIAO; Patrick VIOLETTE; Justin VASQUEZ - Web page: https://patentgrep.com/patent/US20260065484A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/0016: {involving temporal comparison} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/486: {involving generating temporal series of image data} - A61B 6/503: {for diagnosis of the heart} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT} - A61B 6/5205: {involving processing of raw data to produce diagnostic data} - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/094: Adversarial learning - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 30/40: for processing medical images, e.g. editing - G16H 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - A61B 6/037: {Emission tomography} - A61B 6/481: {involving the use of contrast agents} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10132: Ultrasound image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30048: Heart; Cardiac - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30104: Vascular flow; Blood flow; Perfusion - G06V 10/82: using neural networks - G06V 2201/031: of internal organs - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks. ## Claims ### Claim 1 A method of training a whole medical image foundation model, the method comprising: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks. ### Claim 2 (depends on 1) The method of claim 1, wherein the deep learning network comprises at least one of: a Convolutional Neural Network, a Graph Convolutional Neural Network, a PointNet, or a Transformer architecture. ### Claim 3 (depends on 1) The method of claim 1, wherein the medical image datasets comprise coronary computed tomography angiography images. ### Claim 4 (depends on 1) The method of claim 1, wherein a first self-supervised learning model is trained using a portion of the local sections of image data corresponding to regions surrounding coronary arteries. ### Claim 5 (depends on 4) The method of claim 4, wherein: at least one further self-supervised learning model is trained using a further portion of the local sections of image data corresponding to at least one other structure in the medical image datasets; and the at least one other structure comprises myocardium. ### Claim 6 (depends on 1) The method of claim 1, wherein the prediction tasks comprise at least one of: predicting if a patient may experience a cardiovascular event, identifying whether a patient has a condition selected from hypertension, hyperlipidemia, or diabetes, recognizing a CT vendor or scanner type, determining patient preparation factors, estimating microvascular resistance reserve values, predicting demographic characteristics, or assessing image quality for Fractional Flow Reserve Computed Tomography analysis. ### Claim 7 (depends on 1) The method of claim 1, further comprising incorporating an unsupervised clustering loss function trained concurrently with the at least one further network or function, wherein the clustering loss function is configured to group patients into clusters with low intra-class variations and high inter-class variations. ### Claim 8 (depends on 1) The method of claim 1, further comprising: freezing networks used to obtain the patient-level representations; and training additional tasks using the patient-level representation. ### Claim 9 A system for training a whole medical image foundation model, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks. ### Claim 10 (depends on 9) The system of claim 9, wherein the deep learning network comprises at least one of: a Convolutional Neural Network, a Graph Convolutional Neural Network, a PointNet, or a Transformer architecture. ### Claim 11 (depends on 9) The system of claim 9, wherein the medical image datasets comprise coronary computed tomography angiography images. ### Claim 12 (depends on 9) The system of claim 9, wherein a first self-supervised learning model is trained using a portion of the local sections of image data corresponding to regions surrounding coronary arteries. ### Claim 13 (depends on 12) The system of claim 12, wherein: at least one further self-supervised learning model is trained using a further portion of the local sections of image data corresponding to at least one other structure in the medical image datasets; and the at least one other structure comprises myocardium. ### Claim 14 (depends on 9) The system of claim 9, wherein the prediction tasks comprise at least one of: predicting if a patient may experience a cardiovascular event, identifying whether a patient has a condition selected from hypertension, hyperlipidemia, or diabetes, recognizing a CT vendor or scanner type, determining patient preparation factors, estimating microvascular resistance reserve values, predicting demographic characteristics, or assessing image quality for Fractional Flow Reserve Computed Tomography analysis. ### Claim 15 (depends on 9) The system of claim 9, further comprising incorporating an unsupervised clustering loss function trained concurrently with the at least one further network or function, wherein the clustering loss function is configured to group patients into clusters with low intra-class variations and high inter-class variations. ### Claim 16 (depends on 9) The system of claim 9, further comprising: freezing networks used to obtain the patient-level representations; and training additional tasks using the patient-level representation. ### Claim 17 A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for training a whole medical image foundation model, the method comprising: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the deep learning network comprises at least one of: a Convolutional Neural Network, a Graph Convolutional Neural Network, a PointNet, or a Transformer architecture. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the medical image datasets comprise coronary computed tomography angiography images. ### Claim 20 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the prediction tasks comprise at least one of: predicting if a patient may experience a cardiovascular event, identifying whether a patient has a condition selected from hypertension, hyperlipidemia, or diabetes, recognizing a CT vendor or scanner type, determining patient preparation factors, estimating microvascular resistance reserve values, predicting demographic characteristics, or assessing image quality for Fractional Flow Reserve Computed Tomography analysis.