Artificial intelligence in coronary computed tomography angiography

Abstract

Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.

Keywords: Coronary computed tomography angiography; Coronary artery disease; Artificial intelligence; Deep learning; Machine learning; Prognosis

Core Tip: The application of artificial intelligence in coronary computed tomography angiography mainly focuses on the following aspects: (1) Studies based on the coronary arteries and plaques for determination of stenosis degree, identification of plaque types, quantification of coronary artery calcium score, prediction of myocardial infarction, and prognosis evaluation; (2) Studies around the perivascular adipose tissue, which were mainly conducted using radiomics analysis and machine learning algorithm, for improvement of risk stratification; and (3) Studies based on the texture analysis of the left ventricular myocardium for assessment of functionally significant stenosis or for prognosis evaluation.