Learning based methods for personalized assessment, long-term prediction and management of atherosclerosis
First Claim
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1. 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.
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Abstract
A computer-implemented method for providing a personalized evaluation of assessment of atherosclerotic plaques for a patient acquiring patient data comprising non-invasive patient data, medical images of the patient, and blood biomarkers. Features of interest are extracted from the patient data and one or more machine learning models are applied to the features of interest to predict one or more measures of interest related to atherosclerotic plaque.
47 Citations
16 Claims
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1. A computer-implemented method for providing a personalized evaluation of assessment of atherosclerotic plaques for a patient, the method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer-implemented method for training a machine learning model to provide a personalized evaluation of assessment of atherosclerotic plaques for a patient, the method comprising:
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generating a database of synthetic data comprising one or more of in silico anatomical models; performing fluid solid growth (FSG) computations for the in silico anatomical models to yield output data; extracting measures of interest from the output data, wherein the measures of interest include a risk of cardiovascular event, plaque composition, plaque evolution, effect of a drug treatment, in-stent restenosis, and lesions requiring sealing; extracting geometric features of the in silico anatomical models and plaque-related features from the database of synthetic data, wherein the plaque-related features include features describing likelihood of plaque development at a particular location, features describing growth speed of the plaque, wherein the plaque-related features are defined based on features describing likelihood of a rupture of the plaque and features describing likelihood of thrombus formation of a plaque surface; and training one or more machine learning models to generate predicted measurements related to atherosclerotic plaque based on the geometric features of the in silico anatomical models trees and the plaque-related features. - View Dependent Claims (14, 15, 16)
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Specification