Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery.

ABSTRACT: In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed

SUBMITTER: Dong P 

PROVIDER: S-EPMC9328568 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature