Wang YY, Liu B, Wang JH. Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination. World J Gastroenterol 2025; 31(36): 111137 [DOI: 10.3748/wjg.v31.i36.111137]
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/
World J Gastroenterol. Sep 28, 2025; 31(36): 111137 Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.111137
Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination
Yang-Yang Wang, Bin Liu, Ji-Han Wang
Yang-Yang Wang, School of Physics and Electronic Information, Yan’an University, Yan’an 716000, Shaanxi Province, China
Bin Liu, Department of Pharmacy, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei Province, China
Ji-Han Wang, Yan’an Medical College, Yan’an University, Yan’an 716000, Shaanxi Province, China
Co-first authors: Yang-Yang Wang and Bin Liu.
Author contributions: Wang YY and Liu B made equal contributions as co-first authors; Wang YY and Wang JH conceptualized and designed the study, and constructed figures presented in this manuscript; Wang YY searched and reviewed published articles, and wrote the original manuscript; Liu B made revisions to the revised manuscript; Wang JH reviewed the original manuscript, and provided the funding acquisition; all authors approved the submitted version.
Supported by Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, No. 2023-KFMS-1.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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/
Received: June 24, 2025 Revised: August 7, 2025 Accepted: August 26, 2025 Published online: September 28, 2025 Processing time: 87 Days and 14.8 Hours
Abstract
Gastrointestinal (GI) diseases, including gastric and colorectal cancers, significantly impact global health, necessitating accurate and efficient diagnostic methods. Endoscopic examination is the primary diagnostic tool; however, its accuracy is limited by operator dependency and interobserver variability. Advancements in deep learning, particularly convolutional neural networks (CNNs), show great potential for enhancing GI disease detection and classification. This review explores the application of CNNs in endoscopic imaging, focusing on polyp and tumor detection, disease classification, endoscopic ultrasound, and capsule endoscopy analysis. We discuss the performance of CNN models with traditional diagnostic methods, highlighting their advantages in accuracy and real-time decision support. Despite promising results, challenges remain, including data availability, model interpretability, and clinical integration. Future directions include improving model generalization, enhancing explainability, and conducting large-scale clinical trials. With continued advancements, CNN-powered artificial intelligence systems could revolutionize GI endoscopy by enhancing early disease detection, reducing diagnostic errors, and improving patient outcomes.
Core Tip: This review summarizes the latest advances in the application of deep learning-based convolutional neural networks in gastrointestinal endoscopy. It highlights convolutional neural networks’ roles in lesion detection, classification, segmentation, and real-time decision support, emphasizing their potential to enhance diagnostic accuracy, reduce variability, and integrate into clinical workflows for improved patient outcomes.
