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 [DOI: 10.35711/aimi.v1.i1.31]
Corresponding Author of This Article
Xue-Li Chen, PhD, Professor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, No. 266, Xinglong Section of Xifeng Road, Xi’an 710126, Shaanxi Province, China. xlchen@xidian.edu.cn
Research Domain of This Article
Cardiac & Cardiovascular Systems
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, 2020; 1(1): 31-39 Published online Jun 28, 2020. doi: 10.35711/aimi.v1.i1.31
Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey
Feng-Jun Zhao, Si-Qi Fan, Jing-Fang Ren, Karen M von Deneen, Xiao-Wei He, Xue-Li Chen
Feng-Jun Zhao, Si-Qi Fan, Jing-Fang Ren, Xiao-Wei He, School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
Feng-Jun Zhao, Si-Qi Fan, Jing-Fang Ren, Xiao-Wei He, Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
Karen M von Deneen, Xue-Li Chen, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
Author contributions: Zhao FJ performed the majority of the writing and the investigation of articles; Fan SQ performed writing for coronary plaque detection; Ren JF performed writing for coronary artery extraction; He XW and von Deneen KM polished the language and expression of the paper; Chen XL checked the organization and revised the writing of the paper.
Supported bythe National Natural Science Foundation of China, Nos. 61971350, 81627807 and 11727813; the National Key R& D Program of China, No. 2016YFC1300300; the China Postdoctoral Science Foundation, No. 2019M653717; Shaanxi Science Funds for Distinguished Young Scholars, No. 2020JC-27; Fok Ying Tung Education Foundation, No. 161104; and Program for the Young Top-notch Talent of Shaanxi Province.
Conflict-of-interest statement: The authors declare they have no conflicts of interest.
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: Xue-Li Chen, PhD, Professor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, No. 266, Xinglong Section of Xifeng Road, Xi’an 710126, Shaanxi Province, China. xlchen@xidian.edu.cn
Received: May 7, 2020 Peer-review started: May 7, 2020 First decision: June 4, 2020 Revised: June 12, 2020 Accepted: June 17, 2020 Article in press: June 17, 2020 Published online: June 28, 2020 Processing time: 63 Days and 19.3 Hours
Abstract
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.
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 survey the machine learning 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 machine learning-based analysis methods.
