Yili Feng, Ruisen Fu, Hao Sun, Xue Wang, Yang Yang, Chuanqi Wen, Yaodong Hao, Yutong Sun, Bao Li, Na Li, Haisheng Yang, Quansheng Feng, Jian Liu, Zhuo Liu, Liyuan Zhang, Youjun Liu
BACKGROUND AND OBJECTIVE: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. METHODS: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis...
January 2024: Artificial Intelligence in Medicine