Machine learning based evaluation of functional index for coronary lesion severity

Abstract

One of the physiology based clinical indices for coronary lesion severity, fractional flow reserve (FFR)is currently the gold standard for identifying the ischemia-causing stenosis in coronary circulation and for deciding revascularization of the clogged artery. In this study, we newly propose a machine learning based FFR prediction approach from geometric features of stenotic lesion and circulation conditions. We generated total 1,116 anatomic vessel models with various geometric features of a stenosis. FFR data were computed by 3D-0D coupled blood flow dynamics simulations. We employed a fully connected deep neural network model with four hidden layers and a sigmoidal activation function. The input layer has six neurons corresponds to geometric features of stenotic lesion as well as aortic pressure. This novel data-driven approach for near-real time assessment of coronary lesion severity has promising potential in on-site routine clinical practices.