Machine learning for diagnosis of coronary artery disease in computed tomography angiography:A survey

摘要

Coronary artery disease(CAD)has become a major illness endangering human health.It mainly manifests as atherosclerotic plaques,especially vulnerable plaques without obvious symptoms in the early stage.Once a rupture occurs,it will lead to severe coronary stenosis,which in turn may trigger a major adverse cardiovascular event.Computed tomography angiography(CTA)has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution,noninvasiveness,and three-dimensional imaging.However,manual examination of CTA images by radiologists has been proven to be tedious and time-consuming,which might also lead to intra-and interobserver errors.Nowadays,many machine learning algorithms have enabled the(semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images.This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images,including coronary artery extraction,coronary plaque detection,vulnerable plaque identification,and coronary stenosis assessment.Most included articles were published within this decade and are found in the Web of Science.We wish to give readers a glimpse of the current status,challenges,and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.