Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	116570
Country	Algeria

Category	Pathology
Manuscript Type	Minireviews
Article Title	Artificial intelligence in celiac disease pathology: From digital histomorphology to multi-modal integration
Manuscript Source	Invited Manuscript
All Author List	Hakim Rahmoune, Nada Boutrid and Isra Benchoufi
Funding Agency and Grant Number
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.
Citation	Rahmoune H, Boutrid N, Benchoufi I. Artificial intelligence in celiac disease pathology: From digital histomorphology to multi-modal integration. Artif Intell Gastroenterol 2026; In press

Received	2025-11-14 04:48
Peer-Review Started	2025-11-14 04:48
First Decision by Editorial Office Director	2025-11-26 09:23
Return for Revision	2025-11-26 09:23
Revised	2026-01-08 06:53
Publication Fee Transferred
Second Decision by Editor	2026-01-27 02:40
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-27 05:56
Articles in Press	2026-01-27 05:56
Edit the Manuscript by Language Editor
Typeset the Manuscript

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/
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