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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 by the 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
Core Tip

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.