Publication Name	World Journal of Gastroenterology
Manuscript ID	115297
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Editorial
Article Title	Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation
Manuscript Source	Invited Manuscript
All Author List	Yu-Han Yang
Funding Agency and Grant Number
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
Key Words	Gastrointestinal disease; Endoscopy; Automated segmentation; Deep learning; Multi-class model; Comparative study; Artificial intelligence
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.
Citation	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; In press

Received	2025-10-15 07:07
Peer-Review Started	2025-10-15 07:07
First Decision by Editorial Office Director	2025-11-19 10:37
Return for Revision	2025-11-19 10:37
Revised	2025-11-22 16:18
Publication Fee Transferred
Second Decision by Editor	2026-01-04 02:39
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-04 13:59
Articles in Press	2026-01-04 13:59
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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/
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