Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville.
Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
JAMA Netw Open. 2019 Jun 5;2(6):e195822. doi: 10.1001/jamanetworkopen.2019.5822.
Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap.
To develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue.
DESIGN, SETTING, AND PARTICIPANTS: In this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018.
Classification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models.
Overall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations.
A machine learning-based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells' role in the model's ability to differentiate between these histologically similar diseases.
与营养不良相关的肠病，如环境肠病（EE）和乳糜泻（CD），患儿的十二指肠活检显示出显著的组织病理学重叠。
开发卷积神经网络（CNN）以增强对患病与健康十二指肠组织的病理形态特征的检测。
设计、设置和参与者：在这项前瞻性诊断研究中，一个由 4 个卷积、1 个全连接层和 1 个 softmax 层组成的 CNN 在十二指肠活检图像上进行训练。数据由 3 个地点提供：巴基斯坦卡拉奇的 Aga Khan 大学医院；赞比亚卢萨卡的大学教学医院；以及美国弗吉尼亚大学夏洛茨维尔分校。将 102 名儿童（Aga Khan 大学医院 10 名 EE，大学教学医院 16 名 EE，弗吉尼亚大学 34 名 CD，弗吉尼亚大学 42 名无疾病）的十二指肠活检切片转换为 3118 张图像。CNN 是在弗吉尼亚大学设计和分析的。数据于 2017 年 11 月至 2018 年 2 月收集、准备和分析。
每张图像和每个病例的 CNN 分类准确率以及 CNN 模型的聚合 10 倍交叉验证混淆/误差矩阵确定的错误分类率。
共有 102 名儿童参与了这项研究，中位（四分位间距）年龄为 31.0（20.3-75.5）个月，性别分布大致均衡，其中 53 名男孩（51.9%）。该模型的检测准确率为 93.4%，假阴性率为 2.4%。混淆度量指标表明，大多数错误分类发生在 CD 患者和健康患者之间。可视化特征图激活并学习了独特的模式，包括十二指肠组织中的微观特征，如分泌细胞群体的改变。
开发了一种基于机器学习的组织病理学分析模型，该模型的分类准确率为 93.4%，用于识别和区分 EE 和 CD 患儿的十二指肠活检。CNN 与去卷积网络的结合能够实现特征识别，并突出分泌细胞在模型区分这些组织学相似疾病的能力中的作用。
