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Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory conditionof the esophagus associated with elevated esophageal eosinophils. Second onlyto gastroesophageal reflux disease, EoE is one of the leading causes of chronicrefractory dysphagia in adults and children. EoE diagnosis requires enumeratingthe density of esophageal eosinophils in esophageal biopsies, a somewhatsubjective task that is time-consuming, thus reducing the ability to processthe complex tissue structure. Previous artificial intelligence (AI) approachesthat aimed to improve histology-based diagnosis focused on recapitulatingidentification and quantification of the area of maximal eosinophil density.However, this metric does not account for the distribution of eosinophils orother histological features, over the whole slide image. Here, we developed anartificial intelligence platform that infers local and spatial biomarkers basedon semantic segmentation of intact eosinophils and basal zone distributions.Besides the maximal density of eosinophils (referred to as Peak EosinophilCount [PEC]) and a maximal basal zone fraction, we identify two additionalmetrics that reflect the distribution of eosinophils and basal zone fractions.This approach enables a decision support system that predicts EoE activity andclassifies the histological severity of EoE patients. We utilized a cohort thatincludes 1066 biopsy slides from 400 subjects to validate the system'sperformance and achieved a histological severity classification accuracy of86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approachhighlights the importance of systematically analyzing the distribution ofbiopsy features over the entire slide and paves the way towards a personalizeddecision support system that will assist not only in counting cells but canalso potentially improve diagnosis and provide treatment prediction.

Harnessing Artificial Intelligence to Infer Novel Spatial Biomarkers for the Diagnosis of Eosinophilic Esophagitis