Deep learning-based prediction of coronary artery stenosis resistance

Abstract

This study developed a multi-input back-propagation neural network (BPNN) that can be used to predict coronary artery stenosis resistance by inputting vascular geometric parameters and blood flow. Compared with previous studies, the network developed in this study can accurately and rapidly predict coronary artery stenosis resistance, which can not only meet clinical requirements but also reduce the cost of calculation duration. This study contributes to the noninvasive methods for the numerical calculation of fractional flow reserve derived from coronary CT angiography (FFRCT) and indicates that this technique can potentially be used for evaluating myocardial ischemia.