  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.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Quantitative Methods