Publication Name	World Journal of Gastrointestinal Endoscopy
Manuscript ID	113184
Country	China

Category	Medical Laboratory Technology
Manuscript Type	Editorial
Article Title	Deep learning meets small-bowel capsule endoscopy: A step toward faster and more consistent diagnosis of obscure gastrointestinal bleeding
Manuscript Source	Invited Manuscript
All Author List	Heng-Rui Liu
Funding Agency and Grant Number
Corresponding Author	Heng-Rui Liu, Tenured Professor, Visiting Professor, Cancer Research Institute, Jinan University, Zhongshan Second Road, Yuexiu District, Tianjin 518000, China. lh@yinuobiomedical.cn
Key Words	Lesion detection; Artificial intelligence; Deep learning; Obscure gastrointestinal bleeding; Capsule endoscopy
Core Tip	Small-bowel capsule endoscopy is invaluable for evaluating obscure gastrointestinal bleeding but remains limited by lengthy review times and interobserver variability. This study introduces a deep learning system that integrates gastrointestinal localization with multi-lesion detection in full-length capsule videos, achieving high accuracy and cutting reading time from nearly an hour to just minutes. By closely mimicking human reading workflow and validating across centers, the work highlights a practical step toward clinically deployable artificial intelligence assistance, with the potential to standardize interpretation and improve efficiency in routine practice.
Citation	Liu HR. Deep learning meets small-bowel capsule endoscopy: A step toward faster and more consistent diagnosis of obscure gastrointestinal bleeding. World J Gastrointest Endosc 2025; In press

Received	2025-08-18 01:47
Peer-Review Started	2025-08-18 01:47
To Make the First Decision
Return for Revision	2025-08-25 15:37
Revised	2025-08-25 16:19
Second Decision	2025-09-22 02:45
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-09-22 06:56
Articles in Press	2025-09-22 06:56
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5190 (online)
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