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Goal: Eosinophilic esophagitis (EoE) is an allergic inflammatory conditioncharacterized by eosinophil accumulation in the esophageal mucosa. EoEdiagnosis includes a manual assessment of eosinophil levels in mucosal biopsies- a time-consuming, laborious task that is difficult to standardize. One of themain challenges in automating this process, like many other biopsy-baseddiagnostics, is detecting features that are small relative to the size of thebiopsy. Results: In this work, we utilized hematoxylin- and eosin-stainedslides from esophageal biopsies from patients with active EoE and controlsubjects 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 severaldownscaling and cropping strategies, we show that some of the featurescontributing to the correct classification are global rather than specific,local features. Conclusions: We report the ability of artificial intelligenceto identify EoE using computer vision analysis of esophageal biopsy slides.Further, the DCNN features associated with EoE are based on not only localeosinophils but also global histologic changes. Our approach can be used forother conditions that rely on biopsy-based histologic diagnostics.

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