Learning based methods for personalized assessment, long-term prediction and management of atherosclerosis

A computer-implemented method for providing a personalized evaluation of assessment of atherosclerotic plaques for a patient, the method comprising:

• acquiring patient data comprising non-invasive patient data, medical images of the patient, and blood biomarkers;
• extracting features of interest from the patient data;
• training one or more machine learning models using a database of synthetic data comprising one or more of in silico anatomical models and in vitro anatomical models;
• and
• applying the one or more machine learning models to the features of interest to predict a plurality of measures of interest related to atherosclerotic plaque, wherein the plurality of measures of interest related to atherosclerotic plaque include a risk of cardiovascular event, plaque composition, plaque evolution, effect of a drug treatment, in-stent restenosis, lesions requiring sealing, indication of a future screening data, and effect of a device for therapy, wherein the risk of cardiovascular event includes coronary circulation, cerebral circulation, and peripheral circulation,
• wherein the one or more machine learning models are trained using a process comprising:
• performing fluid solid growth (FSG) computations for the in silico anatomical models or flow experiments for the in vitro anatomical models to yield output data;
• extracting measures of interest from the output data;
• extracting geometric features and plaque-related features from the database of synthetic data;
• and
• training the one or more machine learning models to predict measures of interest related to atherosclerotic plaque using the measures of interest from the output data, the geometric features, and the plaque-related features.