ISSN |
2644-3260 (online) |
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/ |
Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
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Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
Website |
http://www.wjgnet.com |
Category |
Cardiac & Cardiovascular Systems |
Manuscript Type |
Minireviews |
Article Title |
Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey
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Manuscript Source |
Invited Manuscript |
All Author List |
Feng-Jun Zhao, Si-Qi Fan, Jing-Fang Ren, Karen M von Deneen, Xiao-Wei He and Xue-Li Chen |
ORCID |
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Funding Agency and Grant Number |
Funding Agency |
Grant Number |
National Natural Science Foundation of China |
61971350 |
National Natural Science Foundation of China |
81627807 |
National Natural Science Foundation of China |
11727813 |
National Key R&D Program of China |
2016YFC1300300 |
China Postdoctoral Science Foundation |
2019M653717 |
Fok Ying Tung Education Foundation |
161104 |
Program for the Young Top-notch Talent of Shaanxi Province |
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Corresponding Author |
Xue-Li Chen, PhD, Professor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi’an 710126, Shaanxi Province, China. xlchen@xidian.edu.cn |
Key Words |
Machine learning; Deep learning; Coronary artery disease; Atherosclerotic plaque; Vulnerability; Stenosis |
Core Tip |
There are reviews that contributed to the segmentation of the coronary artery, detection of calcified plaques, and calculation of fractional flow reserve. To the best of our knowledge, this is the first paper to report a survey of the machine learning (ML) algorithms for the diagnosis of coronary artery disease in computed tomography angiography images, including extraction of coronary arteries, detection of calcified, soft and mixed plaques, identification of plaque vulnerability features including low density plaque, positive remodeling, spot calcification and napkin ring sign, assessment of both anatomically and hemodynamically significant stenosis, and the challenges and perspectives of these ML-based analysis methods. |
Publish Date |
2020-07-10 05:53 |
Citation |
Zhao FJ, Fan SQ, Ren JF, von Deneen KM, He XW, Chen XL. Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey. Artif Intell Med Imaging 2020; 1(1): 31-39 |
URL |
https://www.wjgnet.com/2644-3260/full/v1/i1/31.htm |
DOI |
https://dx.doi.org/10.35711/aimi.v1.i1.31 |