Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis

作者：Ariel Larey, Eliel Aknin, Nati Daniel, G. A. Osswald, Julie M. Caldwell, Mark Rochman, Tanya Wasserman, Margaret H. Collins, Nicoleta C. Arva, Guang‐Yu Yang, Marc E. Rothenberg, Yonatan Savir · 发表于：Frontiers in Medicine · 年份：2022 · DOI：10.3389/fmed.2022.950728 · 被引用次数：27 · 研究领域：Eosinophilic Esophagitis、IL-33, ST2, and ILC Pathways、Eosinophilic Disorders and Syndromes

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE is a clinicopathologic disorder and the histological portion of the diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies, and evaluating additional features such as basal zone hyperplasia is helpful. However, this task requires time-consuming, somewhat subjective manual analysis, thus reducing the ability to process the complex tissue structure and infer its relationship with the patient's clinical status. Previous artificial intelligence (AI) approaches that aimed to improve histology-based diagnosis focused on recapitulating identification and quantification of the area of maximal eosinophil density, the gold standard manual metric for determining EoE disease activity. However, this metric does not account for the distribution of eosinophils or other histological features, over the whole slide image. Here, we developed an artificial intelligence platform that infers local and spatial biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions. Besides the maximal density of eosinophils [referred to as Peak Eosinophil Count (PEC)] and a maximal basal zone fraction, we identify the value of two additional metrics that reflect t...