A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: comparing diagnostic performance with a panel of expert European and Asian endoscopists
A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: comparing diagnostic performance with a panel of expert European and Asian endoscopists
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DOI：
10.1016/j.gie.2021.01.043
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发表时间：
2021-07-13
影响因子：
7.7
通讯作者：
Haidry, Rehan J.
中科院分区：
文献类型：
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作者：
Everson, Martin A.;Garcia-Peraza-Herrera, Luis;Haidry, Rehan J.
背景和目的：乳头内毛细血管袢（IPCLs）是一种微血管结构，与早期鳞状细胞瘤的浸润深度有关，可以准确预测组织学。人工智能可以提高人类对IPCL模式的识别和对组织学的预测，以便在适当的情况下及时获得早期鳞状细胞瘤的内镜治疗。方法：选取台湾2所专科医院115例患者。放大内镜下的鳞状黏膜窄带成像视频根据其组织学标记为发育不良或正常，IPCL模式由3名经验丰富的临床医生一致划分。利用67,742张高质量放大内窥镜窄带图像进行5倍交叉验证，训练卷积神经网络（CNN）对ipcl进行分类。计算性能指标以获得平均F1评分、准确性、敏感性和特异性。一个由5名亚洲和4名欧洲专家组成的小组使用日本内窥镜学会IPCL分类预测了随机选择的158张图像的组织学；计算准确性、敏感性、特异性、阳性预测值和阴性预测值。结果：欧盟（EU）和亚洲内窥镜专家分别获得了97.0%和98%的F1评分（一种衡量二元分类准确率的方法）。欧盟和亚洲临床医生的敏感性和准确性分别为97%、98%和96.9%、97.1%。CNN平均F1得分为94%，灵敏度为93.7%，准确率为91.7%。我们的CNN以视频速率运行，并生成可用于视觉验证CNN预测的类激活图。结论：我们报告了一种临床可解释的CNN，用于基于IPCL模式实时预测组织学，使用最大的为此目的的图像数据集。我们的CNN达到了与内窥镜专家小组相当的诊断性能。
Background and Aims: Intrapapillary capillary loops (IPCLs) are microvascular structures that correlate with the invasion depth of early squamous cell neoplasia and allow accurate prediction of histology. Artificial intelligence may improve human recognition of IPCL patterns and prediction of histology to allow prompt access to endoscopic therapy for early squamous cell neoplasia where appropriate.Methods: One hundred fifteen patients were recruited at 2 academic Taiwanese hospitals. Magnification endoscopy narrow-band imaging videos of squamous mucosa were labeled as dysplastic or normal according to their histology, and IPCL patterns were classified by consensus of 3 experienced clinicians. A convolutional neural network (CNN) was trained to classify IPCLs, using 67,742 high-quality magnification endoscopy narrow-band images by 5-fold cross validation. Performance measures were calculated to give an average F1 score, accuracy, sensitivity, and specificity. A panel of 5 Asian and 4 European experts predicted the histology of a random selection of 158 images using the Japanese Endoscopic Society IPCL classification; accuracy, sensitivity, specificity, positive and negative predictive values were calculated.Results: Expert European Union (EU) and Asian endoscopists attained F1 scores (a measure of binary classification accuracy) of 97.0% and 98%, respectively. Sensitivity and accuracy of the EU and Asian clinicians were 97%, 98% and 96.9%, 97.1%, respectively. The CNN average F1 score was 94%, sensitivity 93.7%, and accuracy 91.7%. Our CNN operates at video rate and generates class activation maps that can be used to visually validate CNN predictions.Conclusions: We report a clinically interpretable CNN developed to predict histology based on IPCL patterns, in real time, using the largest reported dataset of images for this purpose. Our CNN achieved diagnostic performance comparable with an expert panel of endoscopists.