Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 14, 2021; 27(22): 2979-2993
Published online Jun 14, 2021. doi: 10.3748/wjg.v27.i22.2979
Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer
Yu-Jer Hsiao, Yuan-Chih Wen, Wei-Yi Lai, Yi-Ying Lin, Yi-Ping Yang, Yueh Chien, Aliaksandr A Yarmishyn, De-Kuang Hwang, Tai-Chi Lin, Yun-Chia Chang, Ting-Yi Lin, Kao-Jung Chang, Shih-Hwa Chiou, Ying-Chun Jheng
Yu-Jer Hsiao, Wei-Yi Lai, Yi-Ying Lin, Yi-Ping Yang, Yueh Chien, Aliaksandr A Yarmishyn, De-Kuang Hwang, Tai-Chi Lin, Yun-Chia Chang, Ting-Yi Lin, Kao-Jung Chang, Shih-Hwa Chiou, Ying-Chun Jheng, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
Yu-Jer Hsiao, Yuan-Chih Wen, Wei-Yi Lai, Yi-Ying Lin, Yi-Ping Yang, De-Kuang Hwang, Tai-Chi Lin, Kao-Jung Chang, School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
Yuan-Chih Wen, Department of Medical Education, Taipei Veterans General Hospital, Taipei 112201, Taiwan
Wei-Yi Lai, Yi-Ying Lin, Shih-Hwa Chiou, Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
Yi-Ping Yang, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
Yi-Ping Yang, Critical Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
De-Kuang Hwang, Tai-Chi Lin, Yun-Chia Chang, Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
De-Kuang Hwang, Tai-Chi Lin, Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
Ting-Yi Lin, Department of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
Kao-Jung Chang, Shih-Hwa Chiou, Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
Ying-Chun Jheng, Big Data Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
Author contributions: Chiou SH and Jheng YC designed the conception; Hsiao YJ, Wen YC, Yarmishyn AA and Jheng YC wrote the paper; Lai WY, Lin YY, Yang YP, Chien Y, Hwang DK, Lin TC, Chang YC, Lin TY and Chang KJ collected the data.
Conflict-of-interest statement: The 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: Ying-Chun Jheng, PhD, Research Fellow, Department of Medical Research, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Road, Beitou District, Taipei 112201, Taiwan. cycom1220@gmail.com
Received: February 3, 2021
Peer-review started: February 3, 2021
First decision: February 27, 2021
Revised: March 10, 2021
Accepted: April 22, 2021
Article in press: April 22, 2021
Published online: June 14, 2021
Processing time: 125 Days and 4.3 Hours
Abstract

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.

Keywords: Artificial intelligence; Diagnostic; Therapeutic; Endoscopy; Gastric cancer; Gastritis

Core Tip: Artificial intelligence-assisted endoscopy can assist physicians in the screening and diagnosis of gastric cancer. Most of the systems developed so far, applied in images and videos and using white light imaging and narrow-band imaging endoscopies, have achieved accuracies and sensitivities of at least 80%. However, the efficacy of artificial intelligence applications in gastric cancer depends on its intended role in clinical practice, and there have not been any attempts of clinical trials yet. This review summarizes the existing artificial intelligence applications in gastric cancer and pinpoints future research directions for their clinical practice implementation.