Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features

TL;DR

This study develops a deep learning platform that accurately classifies eosinophilic esophagitis from biopsy slides, highlighting the importance of global features alongside local eosinophil detection for diagnosis.

Contribution

The paper introduces a novel DCNN-based method that combines global and local features for biopsy analysis, improving EoE detection accuracy and offering insights into histologic feature importance.

Findings

Achieved 85% accuracy in classifying EoE biopsies

Identified global histologic features as key contributors

Demonstrated potential for AI in biopsy-based diagnostics

Abstract

Goal: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies - a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. Results: In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. Conclusions: We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.