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DEEP LEARNING TISSUE ANALYSIS DIAGNOSES AND PREDICTS TREATMENT RESPONSE IN EOSINOPHILIC ESOPHAGITIS

Background: Eoinophilic Esophagitis (EoE) is a chronic inflammatory condition diagnosed by >=15 eosinophils (Eos) per high-power field (HPF). There is no gold standard for clinical remission and Eo-associated metrics are poorly correlated with symptoms. Deep learning can be used to explore the relationships of tissue features with clinical response. Objectives: To determine if deep learning can elucidate tissue patterns in EoE that predict treatments or symptoms at remission. Methods: We created two deep learning models using esophageal biopsies from histologically normal and EoE patients: one to identify Eos in esophageal biopsies and a second to broadly classify esophageal tissue as EoE vs. normal. We used these models to analyze biopsies at diagnosis and first remission timepoint, as defined by <15 Eos/HPF, in a subset of 19 treatment-naive patients. Differences in deep learning metrics between patient groups were assessed using Wilcoxon Rank-Sum tests. Results: All initial patients were symptomatic at diagnosis and a majority were still suffering from dysphagia at remission. The Eo identification model had a low mean (SD) error of -0.3 (11.5) Eos/HPF. Higher peak and average Eo counts at diagnosis were associated with higher likelihood of being on a food-elimination diet at remission than steroids or proton-pump inhibitor (p<0.05). The EoE classification model had an F1-score of 0.97 for distinguishing normal tissue from EoE. There was a significant decrease from diagnosis in the percentage of EoE-classified tissue among asymptomatic remission patients (p<0.05). Conclusions: Deep learning may have utility in diagnosing EoE and predicting future treatment response at diagnosis and resolution of symptoms at follow-up.