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8/6/2026 7:34:41 AM | Browse: 3 | Download: 0
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Received |
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2025-11-14 04:48 |
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2025-11-14 04:48 |
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2025-11-26 09:23 |
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2025-11-26 09:23 |
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2026-01-08 06:53 |
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2026-01-27 02:40 |
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2026-01-27 05:56 |
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Articles in Press |
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2026-01-27 05:56 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-02-09 00:54 |
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Publish the Manuscript Online |
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2026-08-06 07:34 |
| ISSN |
2644-3236 (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
|
| 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 |
Pathology |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence in celiac disease pathology: From digital histomorphology to multi-modal integration
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Hakim Rahmoune, Nada Boutrid and Isra Benchoufi |
| Funding Agency and Grant Number |
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| Corresponding Author |
Hakim Rahmoune, Associate Professor, MD, PhD, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, El Bez Campus, Setif 19000, Setif, Algeria. rahmounehakim@gmail.com |
| Key Words |
Celiac disease; Histopathology; Marsh classification; Flow cytometry; Artificial intelligence; Deep learning; Machine learning; Digital pathology |
| Core Tip |
Artificial intelligence (AI), particularly deep learning models, has advanced histopathological diagnosis of celiac disease (CD) by automating objective measurement of villous-crypt ratios, intraepithelial lymphocyte quantification, and Marsh classification. Novel multi-modal integration combining AI-histopathology with AI-analyzed flow cytometry data offers enhanced diagnostic accuracy and objectivity. Despite challenges in model interpretability and clinical workflow integration, AI demonstrates transformative potential in reducing inter-observer variability and improving CD diagnosis reproducibility and efficiency. |
| Publish Date |
2026-08-06 07:34 |
| Citation |
Rahmoune H, Boutrid N, Benchoufi I. Artificial intelligence in celiac disease pathology: From digital histomorphology to multi-modal integration. Artif Intell Gastroenterol 2026; 7(2): 116570
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| URL |
https://www.wjgnet.com/2644-3236/full/v7/i2/116570.htm |
| DOI |
https://doi.org/10.35712/aig.v7.i2.116570 |
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