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World J Gastroenterol. May 28, 2021; 27(20): 2531-2544
Published online May 28, 2021. doi: 10.3748/wjg.v27.i20.2531
Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review
Tao Yan, Pak Kin Wong, Ye-Ying Qin
Tao Yan, School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
Tao Yan, Pak Kin Wong, Ye-Ying Qin, Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
Author contributions: Wong PK and Yan T contributed to concept design and drafted the manuscript; Yan T and Qin YY collected the data; All the authors have approved the final version of the manuscript.
Supported by The Science and Technology Development Fund, Macau SAR, No. 0021/2019/A.
Conflict-of-interest statement: All authors declare no conflict 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: Pak Kin Wong, PhD, Professor, Department of Electromechanical Engineering, University of Macau, Avenida da Universidade, Taipa 999078, Macau, China. fstpkw@um.edu.mo
Received: January 24, 2021
Peer-review started: January 24, 2021
First decision: March 14, 2021
Revised: March 27, 2021
Accepted: April 9, 2021
Article in press: April 9, 2021
Published online: May 28, 2021
Processing time: 116 Days and 6.5 Hours
Core Tip

Core Tip: Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, have revolutionized upper gastrointestinal endoscopy. In recent years, several deep learning-based artificial intelligence systems have emerged in the gastrointestinal community for endoscopic detection of precancerous lesions. The current review provides an analysis and status of the deep learning-based diagnosis of precancerous lesions in the upper gastrointestinal tract and identifies future challenges and recommendations.