A machine learning model of lamina propria fibrosis in eosinophilic esophagitis for prediction of fibrostenotic disease.

ABSTRACT:

Background

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease that can progress to fibrostenotic complications. Lamina propria fibrosis (LPF) plays a critical role in this progression but is difficult to assess reliably in routine biopsies. We aimed to develop and validate an artificial intelligence (AI) model to quantify LPF on hematoxylin and eosin (H&E)-stained slides and to evaluate its ability to predict fibrostenotic disease.

Methods

We used a cloud-based platform (Aiforia Inc., Cambridge, MA, USA) to train a supervised AI model to recognize several histological features of EoE, including LPF. Our validation cohort consisted of 213 esophageal biopsy whole-slide images, including 100 adult and 113 pediatric samples with mucosal eosinophilia, which were

SUBMITTER: Sivasubramaniam P 

PROVIDER: S-EPMC12828521 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature