Hemodynamic Analysis of Carotid Sinus with OpenFOAM and Machine Learning

Abstract

Computational fluid dynamics (CFD) shows promise in aiding clinical methods in the early detection of atherosclerosis when combined with currently popular machine learning algorithms. In this study, fluid-structure interaction (FSI) analysis of the carotid artery was performed by creating three-dimensional patient-specific pre-operation carotid artery models of four different patients which have vessel stenosis or aneurysms. As a result of numerical simulations, the average flow velocity and average pressure of the patients at 80 specific cross-sections were obtained. The simulation results of three patients’ pre-operation were used for learning in the machine learning algorithm. The training data consists of 80% of the numerical values, while the remaining 20% is used for testing. Then, the algorithm was asked to predict the flow velocity values at different cross-sections of the artery. The values obtained as a result of learning were compared with those obtained from numerical simulation. We found the results promising in terms of guiding the clinical decisions.