CeliacNet: Celiac Disease Severity Diagnosis on Duodenal Histopathological Images Using Deep Residual Networks

TL;DR

This paper presents a deep residual network model that accurately classifies celiac disease severity from duodenal histopathological images, achieving high diagnostic accuracy and aiding in clinical diagnosis.

Contribution

The study introduces a novel deep learning approach using residual networks for classifying celiac disease severity from biopsy images, demonstrating high effectiveness.

Findings

Achieved AUC > 0.96 for all severity classes

Validated model on an independent set of 120 images

Demonstrated high diagnostic power for CD severity classification

Abstract

Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the "gold standard" for diagnosis. The mild CD might go unnoticed due to non-specific clinical symptoms or mild histologic features. In our current work, we trained a model based on deep residual networks to diagnose CD severity using a histological scoring system called the modified Marsh score. The proposed model was evaluated using an independent set of 120 whole slide images from 15 CD patients and achieved an AUC greater than 0.96 in all classes. These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images.