Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis

TL;DR

This paper enhances AI-based endoscopic diagnosis of eosinophilic esophagitis by significantly increasing training data and applying advanced models, leading to improved accuracy and interpretability.

Contribution

It introduces a large-scale dataset augmentation and employs a data-efficient transformer model for better EoE phenotyping.

Findings

Increased dataset from 435 to 7050 images.

Improved diagnostic accuracy and robustness.

Enhanced interpretability with attention maps.

Abstract

Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation. Diagnosing EoE usually involves endoscopic inspection of the esophageal mucosa and obtaining esophageal biopsies for histologic confirmation. Recent advances have seen AI-assisted endoscopic imaging, guided by the EREFS system, emerge as a potential alternative to reduce reliance on invasive histological assessments. Despite these advancements, significant challenges persist due to the limited availability of data for training AI models - a common issue even in the development of AI for more prevalent diseases. This study seeks to improve the performance of deep learning-based EoE phenotype classification by augmenting our training data with a diverse set of images from online platforms, public datasets, and electronic textbooks increasing our dataset from 435 to 7050 images. We utilized the Data-efficient Image Transformer for image classification and incorporated attention map visualizations to boost interpretability. The findings show that our expanded dataset and model enhancements improved diagnostic accuracy, robustness, and comprehensive analysis, enhancing patient outcomes.