Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 14, 2024; 30(2): 170-183
Published online Jan 14, 2024. doi: 10.3748/wjg.v30.i2.170
Automatic detection of small bowel lesions with different bleeding risks based on deep learning models
Rui-Ya Zhang, Peng-Peng Qiang, Ling-Jun Cai, Tao Li, Yan Qin, Yu Zhang, Yi-Qing Zhao, Jun-Ping Wang
Rui-Ya Zhang, Ling-Jun Cai, Yan Qin, Yu Zhang, Yi-Qing Zhao, Jun-Ping Wang, Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
Peng-Peng Qiang, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi Province, China
Tao Li, School of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang 157011, Heilongjiang Province, China
Author contributions: Zhang RY collected the patients’ clinical data and wrote the paper; Wang JP designed the report and revised the paper; Qiang PP and Cai LJ analyzed the data; Qiang PP, Cai LJ, and Li T revised the paper for important intellectual content; Qin Y, Zhang Y, and Zhao YQ collected the patients’ clinical data.
Supported by The Shanxi Provincial Administration of Traditional Chinese Medicine, No. 2023ZYYDA2005.
Institutional review board statement: This study was approved by the Ethics Committee of the Shanxi Provincial People’s Hospital [(2023)No.360].
Informed consent statement: Each patient provided written informed consent for inclusion in the study.
Conflict-of-interest statement: The authors declare having no conflicts of interest.
Data sharing statement: The datasets generated and/or analyzed during the current 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: Jun-Ping Wang, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, No. 29 Shuangtasi Street, Taiyuan 030012, Shanxi Province, China. wangjp8396@sxmu.edu.cn
Received: November 8, 2023
Peer-review started: November 8, 2023
First decision: December 7, 2023
Revised: December 15, 2023
Accepted: December 26, 2023
Article in press: December 26, 2023
Published online: January 14, 2024
Core Tip

Core Tip: In clinical practice, capsule endoscopy is often used to detect small bowel (SB) lesions and find the cause of bleeding. Here, we have proposed a classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately. This model can enhance the diagnostic efficiency of physicians and improve the ability of physicians to identify high-risk bleeding groups.