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Articles Published Processes
2/11/2026 7:19:23 AM | Browse: 1 | Download: 1
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Received |
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2025-10-15 07:07 |
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Peer-Review Started |
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2025-10-15 07:07 |
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First Decision by Editorial Office Director |
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2025-11-19 10:37 |
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Return for Revision |
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2025-11-19 10:37 |
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Revised |
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2025-11-22 16:18 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-01-04 02:39 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-01-04 13:59 |
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Articles in Press |
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2026-01-04 13:59 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-02-04 00:31 |
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Publish the Manuscript Online |
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2026-02-11 07:19 |
| 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/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yu-Han Yang |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2026-02-11 07:19 |
| 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; 32(8): 115297 |
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i8/115297.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v32.i8.115297 |
© 1993-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
