  Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is
increasing in prevalence. The diagnostic gold-standard involves manual review
of a patient's biopsy tissue sample by a clinical pathologist for the presence
of 15 or greater eosinophils within a single high-power field (400x
magnification). Diagnosing EoE can be a cumbersome process with added
difficulty for assessing the severity and progression of disease. We propose an
automated approach for quantifying eosinophils using deep image segmentation. A
U-Net model and post-processing system are applied to generate eosinophil-based
statistics that can diagnose EoE as well as describe disease severity and
progression. These statistics are captured in biopsies at the initial EoE
diagnosis and are then compared with patient metadata: clinical and treatment
phenotypes. The goal is to find linkages that could potentially guide treatment
plans for new patients at their initial disease diagnosis. A deep image
classification model is further applied to discover features other than
eosinophils that can be used to diagnose EoE. This is the first study to
utilize a deep learning computer vision approach for EoE diagnosis and to
provide an automated process for tracking disease severity and progression.
