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 [DOI: 10.35712/aig.v7.i2.116570]
Corresponding Author of This Article
Hakim Rahmoune, MD, PhD, Associate Professor, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, El Bez Campus, Setif 19137, Algeria. rahmounehakim@gmail.com
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Pathology
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review-article
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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 [DOI: 10.35712/aig.v7.i2.116570]
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 116570 Published online Aug 8, 2026. doi: 10.35712/aig.v7.i2.116570
Artificial intelligence in celiac disease pathology: From digital histomorphology to multi-modal integration
Hakim Rahmoune, Nada Boutrid, Isra Benchoufi
Hakim Rahmoune, Nada Boutrid, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, Setif 19137, Algeria
Isra Benchoufi, Department of Artificial Intelligence, National School of Artificial Intelligence, Algiers 16000, Algiers, Algeria
Co-first authors: Hakim Rahmoune and Nada Boutrid.
Author contributions: Rahmoune H contributed to conceptualization, supervision and validation; Rahmoune H, Boutrid N and Benchoufi I contributed to data curation; Rahmoune H and Boutrid N have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper.
AI contribution statement: Claude AI (Anthropic) and Perplexity AI were used during the preparation of this manuscript. AI tools were employed solely to refine and improve the language of pre-existing author-drafted text. These tools did not independently generate scientific content. All AI-assisted text was carefully reviewed, revised as necessary, and fully endorsed by all authors, who take complete responsibility for the integrity and accuracy of the work as submitted.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Hakim Rahmoune, MD, PhD, Associate Professor, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, El Bez Campus, Setif 19137, Algeria. rahmounehakim@gmail.com
Received: November 14, 2025 Revised: January 8, 2026 Accepted: January 27, 2026 Published online: August 8, 2026 Processing time: 265 Days and 13.7 Hours
Abstract
Celiac disease (CD) diagnosis traditionally relies on small intestinal biopsy evaluation, a process limited by subjectivity and inter-observer variability. This mini-review synthesizes recent artificial intelligence (AI) advancements in CD histopathological diagnosis, based on in-depth search of PubMed, PMC, and Scopus. Key methodological advances include deep learning approaches such as Convolutional Neural Networks and U-Net for automated villous-crypt ratio measurement and Marsh classification; object detection and instance segmentation algorithms for intraepithelial lymphocyte quantification; and weakly supervised learning techniques to reduce annotation burden. Beyond traditional histopathology, this article explores a novel multi-modal integration combining AI-driven histopathological analysis with AI-analyzed flow cytometry data, offering enhanced diagnostic accuracy, objectivity, and reproducibility. Such synergistic approaches address critical limitations of manual evaluation including inter-observer variability, diagnostic delays, and incomplete pathological assessment. While persistent challenges remain-including model interpretability, generalizability across diverse datasets and populations, clinical workflow integration, and regulatory approval-AI demonstrates a transformative potential in achieving objective, reproducible, and efficient CD diagnosis, paving the way for improved patient care and enhanced clinical decision-support.
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.
