Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head. In this work, we train a model to predict the severity of the stenosis blockage based on SRUC criteria variables and other patient information. We implement classic machine learning methods, decision trees and random forests, used in a previous experiment. In addition, we improve the accuracy through the use of the state-of-art Augmented Neural ODE deep learning method. Through systematic and theory-rooted analysis, we examine different parameters to achieve an accuracy of about 77%. These results show the strong potential in applying recently developing deep learning methods, while simultaneously suggesting that the current data provided by the SRUC criteria may be insufficient to predict stenosis severity at a high performance level.
@article{arxiv.2108.00296,
title = {Deep Predictive Learning of Carotid Stenosis Severity},
author = {Yiqun Diao and Oliver Zhao and Priya Kothapalli and Peter Monteleone and Chandrajit Bajaj},
journal= {arXiv preprint arXiv:2108.00296},
year = {2021}
}