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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
Abstract

Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.

Keywords: Artificial intelligence; Deep learning; Convolutional neural network; Precancerous lesions; Endoscopy

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.