Prediction of time averaged wall shear stress distribution in coronary arteries' bifurcation varying in morphological features via deep learning.

ABSTRACT:

Introduction

Understanding the hemodynamics of blood circulation is crucial to reveal the processes contributing to stenosis and atherosclerosis development.

Method

Computational fluid dynamics (CFD) facilitates this understanding by simulating blood flow patterns in coronary arteries. Nevertheless, applying CFD in fast-response scenarios presents challenge due to the high computational costs. To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries' bifurcation.

Results

To prepare the dataset, 1800 idealized models with varying morphological parameters are created. Afterward, we design a CNN-based U

SUBMITTER: Sarkhosh MH 

PROVIDER: S-EPMC11920710 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature