Opinion Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Jun 28, 2021; 2(3): 50-62
Published online Jun 28, 2021. doi: 10.37126/aige.v2.i3.50
Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection
Yi-Fan Lu, Bin Lyu
Yi-Fan Lu, Bin Lyu, Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
Author contributions: Lu YF contributed to bibliographic retrieval, data compilation, methodology, software, and manuscript drafting; Lyu B reviewed and proofread the manuscript; all authors contributed to manuscript editing; all authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 81770535 and No. 81970470.
Conflict-of-interest statement: The authors declare no conflict of interests for this article.
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: Bin Lyu, MD, Chief Doctor, Professor, Department of Gastroenterology, First Affiliated Hospital of Zhejiang Chinese Medical University, No. 54 Youdian Road, Shangcheng District, Hangzhou 310006, Zhejiang Province, China. lvbin@medmail.com.cn
Received: May 2, 2021
Peer-review started: May 2, 2021
First decision: May 19, 2021
Revised: June 1, 2021
Accepted: June 18, 2021
Article in press: June 18, 2021
Published online: June 28, 2021
Processing time: 65 Days and 5.9 Hours
Abstract

With the appearance and prevalence of deep learning, artificial intelligence (AI) has been broadly studied and made great progress in various fields of medicine, including gastroenterology. Helicobacter pylori (H. pylori), closely associated with various digestive and extradigestive diseases, has a high infection rate worldwide. Endoscopic surveillance can evaluate H. pylori infection situations and predict the risk of gastric cancer, but there is no objective diagnostic criteria to eliminate the differences between operators. The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H. pylori infection, which is superior to novice endoscopists and similar to skilled. Compared with the visual diagnosis of H. pylori infection by endoscopists, AI possesses voluminous advantages: High accuracy, high efficiency, high quality control, high objectivity, and high-effect teaching. This review summarizes the previous and recent studies on AI-assisted diagnosis of H. pylori infection, points out the limitations, and puts forward prospect for future research.

Keywords: Artificial intelligence; Helicobacter pylori; Endoscopy; Diagnosis; Deep learning; Machine learning

Core Tip: In recent years, artificial intelligence (AI) has been rapidly developed and applied in various fields of medicine, including gastroenterology. We witnessed the promising application of AI in endoscopic diagnosis of Helicobacter pylori infection. In this review, we summarize the advantages of AI, point out the limitations of current studies, and put forward the direction of future research.