Systems, devices, and methods for non-invasive image-based plaque analysis and risk determination

A computer-implemented method of determining a prescribed flow reserve of a patient based on a patient-specific maximum physical activity state to facilitate determination of a cardiovascular disease treatment for the patient, the method comprising:

• accessing, by a computer system, data indicative of a lifestyle pattern of a patient, the data comprising one or more of heart rate, blood oxygen level, respiratory rate, or survey data;
• determining, by the computer system, a maximum physical activity state for the patient that meets a predetermined confidence threshold, the maximum physical activity state for the patient determined based at least in part on the data indicative of the lifestyle pattern of the patient;
• accessing, by the computer system, a medical image of the patient, wherein the medical image is obtained non-invasively;
• analyzing, by the computer system, the medical image of the patient to identify a plurality of vessels, the plurality of vessels comprising a first vessel and a second vessel, wherein the first vessel is a different from than the second vessel;
• identifying, by the computer system using at least in part a first machine learning algorithm, a first parameter for the first vessel comprising at least one of:
• a lumen wall of the first vessel, a vessel wall of the first vessel, or presence of plaque in the first vessel;
• identifying, by the computer system using at least in part the first machine learning algorithm, a second parameter for the second vessel comprising at least one of:
• a lumen wall of the second vessel, a vessel wall of the second vessel, or a presence of plaque in the second vessel;
• applying, a second machine learning algorithm to determine, by the computer system, a prescribed flow reserve in the first vessel at the maximum physical activity state based on the first parameter for the first vessel and the second parameter for the second vessel, wherein prescribed flow reserve comprises a ratio or percentage of coronary blood flow rate of the patient at the maximum physical activity state against a reference coronary blood flow rate at the maximum physical activity state
• wherein the second machine learning algorithm is trained on a dataset to accept the plurality of variables as inputs and to output the prescribed flow reserve,
• wherein a value of the second parameter of the second vessel impacts blood flow within the first vessel and the second vessel, and
• wherein a value of the first parameter of the first vessel impacts blood flow within the first vessel and second vessel, wherein the first vessel and the second vessel comprise different portions of a network of vessels;
• and
• predicting, by the computer system, an impact of a proposed treatment on the prescribed flow reserve of the patient at the maximum physical activity state, wherein the impact of the proposed treatment on the prescribed flow reserve of the patient is predicted based at least in part on a plurality of reference data derived from a plurality of subjects treated with the proposed treatment with varying prescribed flow reserves, wherein the predicted impact of the proposed treatment on the prescribed flow reserve of the patient at the maximum physical activity state is used to determine whether to treat the patient with the proposed treatment,
• wherein the computer system comprises a computer processor and an electronic storage medium.