| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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
|
| 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 |
Clinical Trials Study |
| Article Title |
Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Wen Chen, Meng Wu, Heng-Yu Wang, Hong-Bo Wu, Jie Li, Yu-Hao Sun, Fang Huang, Min Gao, Zhi-Hang Zhong, Yan-Min Wu and Lei Chen |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Chongqing Science and Health Joint Medical Research Project |
No. 2023ZDXM007 |
|
| Corresponding Author |
Lei Chen, Full Professor, MD, PhD, Department of Gastroenterology, Southwest Hospital of Army Medical University, No. 30 Gaotanyan, Shapingba District, Chongqing 400038, China. xhl13228683896@tmmu.edu.cn |
| Key Words |
Colonoscopy; Artificial intelligence; Mucosa touch rate; Polyp detection rate; Adenoma detection rate |
| Core Tip |
This study introduces an artificial intelligence-based mucosa touch rate (MTR) as a novel real-time quality indicator for colonoscopy. Using a deep learning model, MTR objectively quantifies mucosal contact during withdrawal. The results demonstrate a strong negative correlation between MTR and polyp detection rate (PDR). Prospective validation shows that real-time MTR feedback significantly improves PDR, particularly among less experienced endoscopists, highlighting its potential for real-time skill assessment and standardized training in colonoscopy quality control. |
| Publish Date |
2026-07-14 07:33 |
| Citation |
Chen W, Wu M, Wang HY, Wu HB, Li J, Sun YH, Huang F, Gao M, Zhong ZH, Wu YM, Chen L. Artificial intelligence-based mucosa touch rate: A novel real-time quality control indicator for colonoscopy. World J Gastroenterol 2026; 32(27): 119276
|
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i27/119276.htm |
| DOI |
https://doi.org/10.3748/wjg.119276 |
