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

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	2025-10-09 08:24
Publish the Manuscript Online	2025-10-16 06:50

ISSN	1948-5190 (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: https://creativecommons.org/Licenses/by-nc/4.0/
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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.
Publish Date	2025-10-16 06:50
Citation	<p>Liu HR. Deep learning meets small-bowel capsule endoscopy: A step toward faster and more consistent diagnosis of obscure gastrointestinal bleeding. <i>World J Gastrointest Endosc</i> 2025; 17(10): 113184</p>
URL	https://www.wjgnet.com/1948-5190/full/v17/i10/113184.htm
DOI	https://dx.doi.org/10.4253/wjge.v17.i10.113184

Full Article (PDF)	WJGE-17-113184-with-cover.pdf
Manuscript File	113184_Auto_Edited_094239.docx
Answering Reviewers	113184-answering-reviewers.pdf
Audio Core Tip	113184-audio.mp3
Conflict-of-Interest Disclosure Form	113184-conflict-of-interest-statement.pdf
Copyright License Agreement	113184-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	113184-non-native-speakers.pdf
Peer-review Report	113184-peer-reviews.pdf
Scientific Misconduct Check	113184-scientific-misconduct-check.png
Scientific Editor Work List	113184-scientific-editor-work-list.pdf
CrossCheck Report	113184-crosscheck-report.pdf