A machine learning model of lamina propria fibrosis in eosinophilic esophagitis for prediction of fibrostenotic disease
This study demonstrates that AI-based quantification of LPF on routine H&E slides provides an objective and clinically meaningful assessment of fibrosis in EoE. The AI fibrosis score predicts fibrostenotic disease more consistently than conventional pathology evaluation and may improve risk stratification even in limited biopsy samples. Integration of digital pathology tools may enhance histological assessment of fibrosis in EoE and support clinical decision-making.
Authors
- Rofyda Elhalaby (ORCID: https://orcid.org/0000-0001-6990-8154)
- Taofic Mounajjed
- Puanani Hopson (ORCID: https://orcid.org/0000-0002-2723-3635)
- Stephen M. Lagana
- Abdelrahman Shabaan
- Rondell Patrell Graham (ORCID: https://orcid.org/0000-0002-8686-4867)
- Christopher Hartley (ORCID: https://orcid.org/0000-0002-2237-3102)
- Byoung Uk Park (ORCID: https://orcid.org/0000-0002-8797-2026)
- Priyadharshini Sivasubramaniam (ORCID: https://orcid.org/0000-0001-6058-2688)
- Imad Absah (ORCID: https://orcid.org/0000-0002-7565-870X)
- Bashar Hasan (ORCID: https://orcid.org/0000-0001-9531-4990)
- Chamil C. Codipilly
- Ameya Patil (ORCID: https://orcid.org/0000-0003-4666-2205)
- A Dacosta
- Saadiya Nazli
- Lindsey Smith
- Roger K. Moreira
Institutions
- University of Minnesota (US)
- Mayo Clinic (US)
- Medical College of Wisconsin (US)
- Columbia University Irving Medical Center (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- University of Virginia (US)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1016/j.jpi.2025.100538
- Primary Topic
- Eosinophilic Esophagitis
- Type
- article
- Field-Weighted Citation Impact
- 0.00