利用深度学习计算机视觉推进嗜酸性粒细胞食管炎诊断和表型评估。

William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, Donald E Brown
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引用次数: 0

摘要

嗜酸性粒细胞食管炎(EoE)是一种炎症性食管疾病,发病率越来越高。诊断的黄金标准是由临床病理学家对患者的活检组织样本进行人工检查,以确定在单个高倍视野(400 倍放大率)内是否存在 15 个或更多的嗜酸性粒细胞。嗜酸性粒细胞增多症的诊断是一个繁琐的过程,给评估疾病的严重程度和进展增加了难度。我们提出了一种利用深度图像分割量化嗜酸性粒细胞的自动化方法。应用 U-Net 模型和后处理系统可生成基于嗜酸性粒细胞的统计数据,从而诊断咽喉炎并描述疾病的严重程度和进展情况。这些统计数据是在最初诊断咽喉炎时从活检中获取的,然后与患者元数据(临床和治疗表型)进行比较。这样做的目的是找到联系,以便在新患者初次确诊疾病时为其治疗计划提供潜在指导。该研究还进一步应用了深度图像分类模型,以发现除嗜酸性粒细胞以外可用于诊断咽喉炎的其他特征。这是第一项将深度学习计算机视觉方法用于咽喉炎诊断的研究,也是第一项为跟踪疾病严重程度和进展提供自动化流程的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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