Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks.
Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks.
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DOI：
10.1007/978-3-030-32520-6_55
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Brown DE
中科院分区：
文献类型：
--
作者：
Kowsari K;Sali R;Khan MN;Adorno W;Ali SA;Moore SR;Amadi BC;Kelly P;Syed S;Brown DE
乳糜泻（CD）和环境性肠病（EE）是营养不良的常见病因，对儿童正常发育产生不利影响。乳糜泻是一种自身免疫性疾病，在全球范围内普遍存在，由对麸质敏感性增加引起。麸质暴露会破坏小肠上皮屏障，导致营养吸收不良和儿童营养不良。环境性肠病也会导致屏障功能障碍，但被认为是由对感染的易感性增加所致。在中低收入国家，环境性肠病被认为是营养不良、口服疫苗失效和认知发育受损的主要原因。这两种疾病都需要进行组织活检来诊断，而解读临床活检图像以区分这些胃肠道疾病的一个主要挑战是它们之间显著的组织病理学重叠。在当前研究中，我们提出一种卷积神经网络（CNN）来对乳糜泻、环境性肠病患者以及健康对照者的十二指肠活检图像进行分类。我们使用包含1000张活检图像的大型队列评估了我们所提出模型的性能。我们的评估结果显示，所提出的模型对乳糜泻、环境性肠病和健康对照的受试者工作特征曲线下面积分别达到0.99、1.00和0.97。这些结果证明了所提出模型在十二指肠活检分类中的判别能力。
Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.
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