Deep learning-based localization and lesion detection in capsule endoscopy for patients with suspected small-bowel bleeding

doi:10.3748/wjg.v31.i27.106819

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

World J Gastroenterol. Jul 21, 2025; 31(27): 106819
Published online Jul 21, 2025. doi: 10.3748/wjg.v31.i27.106819

Deep learning-based localization and lesion detection in capsule endoscopy for patients with suspected small-bowel bleeding

Yeong Seok Kwon, Tae Yong Park, So Eui Kim, Yehyun Park, Jae Gon Lee, Sang Pyo Lee, Kyeong Ok Kim, Hyun Joo Jang, Young Joo Yang, Bum-Joo Cho

Yeong Seok Kwon, Young Joo Yang, Department of Internal Medicine, Hallym University College of Medicine, Chuncheon-si 24253, South Korea

Tae Yong Park, So Eui Kim, Bum-Joo Cho, Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang 14068, South Korea

Yehyun Park, Department of Internal Medicine, Ewha Womans University Seoul Hospital, Seoul 07804, South Korea

Jae Gon Lee, Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri 11923, South Korea

Sang Pyo Lee, Department of Internal Medicine, Hanyang University College of Medicine, Seoul 04763, South Korea

Kyeong Ok Kim, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu 42415, South Korea

Hyun Joo Jang, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong 18450, South Korea

Bum-Joo Cho, Department of Ophthalmology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, South Korea

Co-first authors: Yeong Seok Kwon and Tae Yong Park.

Co-corresponding authors: Young Joo Yang and Bum-Joo Cho.

Author contributions: Yang YJ, Cho BJ contributed to the conceptualization and supervision of the manuscript; Yang YJ, Park TY was involved in the validation of this study; Kwon YS, Park Y, Lee JG, Lee SP, Kim KO, Jang HJ participated in the data curation; Kwon YS, Park TY, Kim SE contributed to the investigation; Kwon YS, Park TY took part in the visualization and contributed to the writing-original draft preparation; Yang YJ, Cho BJ, Park TY contributed to the methodology of the manuscript; Park TY, Kim SE was responsible to the software; Yang YJ, Cho BJ contributed to the writing-reviewing and editing.

Supported by The Bio and Medical Technology Development Program of the National Research Foundation, No. NRF-2022R1C1C1010643.

Institutional review board statement: The study was approved by the Institutional Review Board of Chuncheon Sacred Heart Hospital (IRB No. 2018-05).

Informed consent statement: All of the patients’ personal identifiable information was removed before use, and individual consent for this retrospective analysis was waived.

Conflict-of-interest statement: The authors declare that they have no conflict of interest.

Data sharing statement: The data sets generated and/or analyzed during this study are available from the corresponding author on reasonable request.

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: Young Joo Yang, Assistant Professor, Department of Internal Medicine, Hallym University College of Medicine, 77 Sakju-ro, Chuncheon-si 25253, South Korea. yjyang@hallym.ac.kr

Received: March 9, 2025
Revised: May 2, 2025
Accepted: July 1, 2025
Published online: July 21, 2025
Processing time: 135 Days and 22.4 Hours

Core Tip

Core Tip: Small-bowel capsule endoscopy (SBCE) is essential for diagnosing obscure gastrointestinal bleeding (OGIB), but its analysis is time-intensive and dependent on the reader's expertise. Despite advancements in artificial intelligence (AI), few models combine accurate small-bowel (SB) localization and abnormality detection. We developed an AI model that automatically distinguishes the SB from the stomach and colon and diagnoses SB abnormalities such as erosions/ulcers, angiodysplasia, and bleeding in patients with suspected OGIB. Our AI model significantly decreased SBCE reading time compared to that of conventional reading with comparable performance for SB abnormality detection, demonstrating the efficiency and reliability of AI integration into SBCE reading.