An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions.

Journal Article (Journal Article)

Coronary bifurcation lesions are a leading cause of Coronary Artery Disease (CAD). Despite its prevalence, coronary bifurcation lesions remain difficult to treat due to our incomplete understanding of how various features of lesion anatomy synergistically disrupt normal hemodynamic flow. In this work, we employ an interpretable machine learning algorithm, the Classification and Regression Tree (CART), to model the impact of these geometric features on local hemodynamic quantities. We generate a synthetic arterial database via computational fluid dynamic simulations and apply the CART approach to predict the time averaged wall shear stress (TAWSS) at two different locations within the cardiac vasculature. Our experimental results show that CART can estimate a simple, interpretable, yet accurately predictive nonlinear model of TAWSS as a function of such features.Clinical relevance- The fitted tree models have the potential to refine predictions of disturbed hemodynamic flow based on an individual's cardiac and lesion anatomy and consequently makes progress towards personalized treatment planning for CAD patients.

Full Text

Duke Authors

Cited Authors

  • Liu, X; Vardhan, M; Wen, Q; Das, A; Randles, A; Chi, EC

Published Date

  • November 2021

Published In

  • Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference

Volume / Issue

  • 2021 /

Start / End Page

  • 4432 - 4435

PubMed ID

  • 34892203

Electronic International Standard Serial Number (EISSN)

  • 2694-0604

International Standard Serial Number (ISSN)

  • 2375-7477

Digital Object Identifier (DOI)

  • 10.1109/embc46164.2021.9631082


  • eng