Deep learning‐based surrogate modeling of coronary in‐stent restenosis

Abstract

AbstractCoronary artery disease (CAD) is one of the largest causes of death worldwide. Percutaneous coronary intervention (PCI) is a minimally invasive procedure to restore blood flow in blocked coronary arteries. However, PCI carries risks such as in‐stent restenosis and thrombosis. Drug‐eluting stents were developed to counteract the restenosis observed after stent implantation. An effective in silico model that can accurately predict the restenosis procedure is of great importance for the cardiology. This study aims to develop a deep learning‐based surrogate model for in‐stent restenosis incorporating anti‐inflammatory drugs embedded in the drug‐eluting stents. The model includes a detailed multiphysics approach based on partial differential equations (PDEs) to capture platelet aggregation, growth‐factor release, cellular motility and drug deposition.