05-03-2015 дата публикации
Номер: US20150065846A1
Принадлежит:
Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model. 129-. (canceled)30. A computer-implemented method for predicting information relating to a vascular lesion of a patient , the method comprising:receiving, for each of a plurality of individuals, a geometric model including a plurality of points, and an estimate of a probability of plaque growth, shrinkage, or onset at each of the plurality of points;receiving an image of a patient's vasculature;identifying a region of the patient's vasculature as being a possible location of plaque, based on a comparison between the image of the patient's vasculature and the geometry of one or more individuals' geometric models; andgenerating, using a computer processor, a prediction or a probability of a risk of artery disease for the patient at the possible location of plaque, based, at least in part, on one or more of the estimates of probability of plaque growth, shrinkage, or onset at one or more of the plurality of points of the individuals' geometric models.31. The method of claim 30 , further ...
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