Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2(3): 73-85 [DOI: 10.35711/aimi.v2.i3.73]
Corresponding Author of This Article
Yang Hou, PhD, Professor, Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang 110004, Liaoning Province, China. houyang1973@163.com
Research Domain of This Article
Medical Laboratory Technology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Jun 28, 2021; 2(3): 73-85 Published online Jun 28, 2021. doi: 10.35711/aimi.v2.i3.73
Artificial intelligence in coronary computed tomography angiography
Zhe-Zhe Zhang, Yan Guo, Yang Hou
Zhe-Zhe Zhang, Yang Hou, Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
Yan Guo, GE Healthcare, Beijing 100176, China
Author contributions: Zhang ZZ performed the majority of literature search and manuscript revision, and prepared the figures and tables; Guo Y performed data acquisition and coordinated the writing; Hou Y read and approved the final manuscript.
Supported bythe National Natural Science Foundation of China, No. 82071920 and No. 81901741; and the Key Research & Development Plan of Liaoning Province, No. 2020JH2/10300037.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors who contributed their efforts in this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yang Hou, PhD, Professor, Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang 110004, Liaoning Province, China. houyang1973@163.com
Received: May 22, 2021 Peer-review started: May 22, 2021 First decision: June 16, 2021 Revised: June 20, 2021 Accepted: July 2, 2021 Article in press: July 2, 2021 Published online: June 28, 2021
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.
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.
