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; 17(10): 113184 [DOI: 10.4253/wjge.v17.i10.113184]
Corresponding Author of This Article
Heng-Rui Liu, Researcher, Cancer Research Institute, Jinan University, No. 601 Huangpu West Avenue, Tianhe District, Guangzhou 510632, Guangdong Province, China. lh@yinuobiomedical.cn
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Medical Laboratory Technology
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Editorial
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Oct 16, 2025 (publication date) through Oct 19, 2025
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Publication Name
World Journal of Gastrointestinal Endoscopy
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1948-5190
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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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; 17(10): 113184 [DOI: 10.4253/wjge.v17.i10.113184]
World J Gastrointest Endosc. Oct 16, 2025; 17(10): 113184 Published online Oct 16, 2025. doi: 10.4253/wjge.v17.i10.113184
Deep learning meets small-bowel capsule endoscopy: A step toward faster and more consistent diagnosis of obscure gastrointestinal bleeding
Heng-Rui Liu
Heng-Rui Liu, Cancer Research Institute, Jinan University, Guangzhou 510632, Guangdong Province, China
Author contributions: Liu HR finished the paper.
Conflict-of-interest statement: The author declare that has no conflict of interest.
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/
Corresponding author: Heng-Rui Liu, Researcher, Cancer Research Institute, Jinan University, No. 601 Huangpu West Avenue, Tianhe District, Guangzhou 510632, Guangdong Province, China. lh@yinuobiomedical.cn
Received: August 18, 2025 Revised: August 25, 2025 Accepted: September 22, 2025 Published online: October 16, 2025 Processing time: 59 Days and 10.2 Hours
Abstract
Small-bowel capsule endoscopy (SBCE) is the first-line diagnostic tool for obscure gastrointestinal bleeding but is limited by labor-intensive review, reader dependency, and interobserver variability. In this issue, Kwon et al present a convolutional neural network-based system capable of both gastrointestinal segment localization and multi-lesion detection, erosions/ulcers, angiodysplasia, and bleeding, using full-length SBCE videos. The model achieved > 97% localization accuracy and approximately 99% lesion detection accuracy in internal testing, and in external validation reduced reading time from nearly one hour to nine minutes without compromising detection performance. Methodological strengths include a two-step detection-classification pipeline, temporal smoothing, and ensemble learning, closely simulating human reading workflow. Compared with previous artificial intelligence (AI)-SBCE studies, this integrated “localization + detection” approach represents a step toward clinically deployable AI assistance. However, generalizability remains limited by single-platform training, a focus on three lesion types, and modest external validation size. Wider lesion coverage, multi-platform validation, and real-time integration will be essential for broader adoption. This study underscores the potential of AI to improve efficiency and consistency in SBCE interpretation and marks meaningful progress toward its routine clinical use.
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.
