| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Artificial intelligence-assisted endoscopists improve the detection rate of high-risk gastric lesions: A propensity score-matched retrospective study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jia-Xiu Ying, Si-Yan Yan, Xin-Yu Fu, Yi-Jing Zhou, Jing-Jing Zhou, Yan Yang, Xian-Bin Zhou, Zhen-Zhen Wang, Shao-Wei Li, Li-Na Fang and Xin-Li Mao |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| “Pioneer” and “Leading Goose” R&D Program of Zhejiang |
No. 2025C02139 |
| Medical Science and Technology Project of Zhejiang Province |
No. 2024KY1788 |
| Program of Taizhou Science and Technology Grant |
No. 22ywb09, No. 23ywa33, No. 23ywa35 |
| Program of Taizhou Science and Technology Grant |
No. 23ywb03 |
| Open Project Program of Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province |
No. 21SZDSYS01 |
|
| Corresponding Author |
Xin-Li Mao, MD, PhD, Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai 317000, Zhejiang Province, China. maoxl@enzemed.com |
| Key Words |
Artificial intelligence; High-risk gastric lesions; Early gastric cancer; Precancerous lesions; Detection rate |
| Core Tip |
Early detection of high-risk gastric lesions remains challenging during routine esophagogastroduodenoscopy. In this large, propensity score-matched cohort study including 15528 patients, we demonstrated that artificial intelligence (AI) significantly improves the detection rate of high-risk gastric lesions, particularly high-grade intraepithelial neoplasia, low-grade intraepithelial neoplasia, and early gastric cancer. Notably, AI assistance was most beneficial when combined with experienced endoscopists and procedures performed under anesthesia. These findings highlight the clinical value of AI as an effective adjunct for enhancing diagnostic accuracy and optimizing endoscopic practice. |
| Publish Date |
2026-05-26 05:34 |
| Citation |
Ying JX, Yan SY, Fu XY, Zhou YJ, Zhou JJ, Yang Y, Zhou XB, Wang ZZ, Li SW, Fang LN, Mao XL. Artificial intelligence-assisted endoscopists improve the detection rate of high-risk gastric lesions: A propensity score-matched retrospective study. World J Gastroenterol 2026; 32(21): 117299 |
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i21/117299.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v32.i21.117299 |
