Yang YH. Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation. World J Gastroenterol 2026; 32(8): 115297 [DOI: 10.3748/wjg.v32.i8.115297]
Corresponding Author of This Article
Yu-Han Yang, MD, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
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/
Feb 28, 2026 (publication date) through Feb 14, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Yang YH. Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation. World J Gastroenterol 2026; 32(8): 115297 [DOI: 10.3748/wjg.v32.i8.115297]
World J Gastroenterol. Feb 28, 2026; 32(8): 115297 Published online Feb 28, 2026. doi: 10.3748/wjg.v32.i8.115297
Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation
Yu-Han Yang
Yu-Han Yang, West China Hospital, Sichuan University, Chengdu 6100041, Sichuan Province, China
Author contributions: Yang YH contributed to conceptualization, writing-original draft preparation, writing-review & editing; Yang YH read and approved the final manuscript and agree to be accountable for all aspects of the work.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yu-Han Yang, MD, West China Hospital, Sichuan University, No. 17 People's South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com
Received: October 15, 2025 Revised: November 22, 2025 Accepted: January 4, 2026 Published online: February 28, 2026 Processing time: 121 Days and 1.5 Hours
Core Tip
Core Tip: This study evaluates 17 advanced deep learning models, including convolutional neural network-, transformer-, and mamba-based architectures, for multi-class upper gastrointestinal disease segmentation. Swin-UMamba achieves the highest segmentation accuracy, while SegFormer balances efficiency and performance. Automated segmentation demonstrates significant clinical value by improving diagnostic precision, reducing missed diagnoses, streamlining treatment planning, and easing physician workload. Key challenges include lighting variability, vague lesion boundaries, multi-label complexities, and dataset limitations. Future directions, such as multi-modal learning, self-supervised techniques, spatio-temporal modeling, and rigorous clinical validation, are essential to enhance model robustness and ensure applicability in diverse healthcare settings for better patient outcomes.
