A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification.

ABSTRACT: Histological assessment is essential for the diagnosis and management of celiac disease. Current scoring systems, including modified Marsh (Marsh-Oberhuber) score, lack inter-pathologist agreement. To address this unmet need, we aimed to develop a fully automated, quantitative approach for histology characterisation of celiac disease. Convolutional neural network models were trained using pathologist annotations of hematoxylin and eosin-stained biopsies of celiac disease mucosa and normal duodenum to identify cells, tissue and artifact regions. Biopsies of duodenal mucosa of varying celiac disease severity, and normal duodenum were collected from a large central laboratory. Celiac disease slides (N = 318) were split into training (n = 230; 72.3%), validation (n = 60; 18.9%) and test (n = 2

SUBMITTER: Griffin M 

PROVIDER: S-EPMC11612272 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature