Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision.

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 (400× 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.

嗜酸性食管炎（EoE）是一种患病率不断上升的炎症性食管疾病。诊断的金标准包括由临床病理学家对患者的活检组织样本进行人工检查，查看在单个高倍视野（400×放大倍数）内是否存在15个或更多的嗜酸性粒细胞。诊断EoE可能是一个繁琐的过程，在评估疾病的严重程度和进展方面还存在额外的困难。我们提出一种使用深度图像分割来量化嗜酸性粒细胞的自动化方法。应用一个U - Net模型和后处理系统来生成基于嗜酸性粒细胞的统计数据，这些数据可用于诊断EoE以及描述疾病的严重程度和进展。这些统计数据是在EoE初次诊断时的活检中获取的，然后与患者的元数据（临床和治疗表型）进行比较。目的是找到可能在新患者初次疾病诊断时指导治疗方案的关联。进一步应用一个深度图像分类模型来发现除嗜酸性粒细胞之外可用于诊断EoE的特征。这是第一项利用深度学习计算机视觉方法进行EoE诊断并提供跟踪疾病严重程度和进展的自动化流程的研究。