【摘要】 目的:探讨卷积神经网络(convolutional neural network, CNN)技术对鼻咽癌窄带成像内镜图像的诊断效能。方法:收集2014-2016年广西壮族自治区人民医院834例鼻咽病变的窄带成像内镜图像和临床病理资料。使用DenseNet 201模型训练分类任务，利用测试数据集对模型进行检测和性能评价，并和内镜医生的判别效果进行比较。结果:CNN诊断鼻咽癌的受试者工作特征曲线下面积为0.98。CNN的敏感度为91.90%,特异度为94.69%,而2名内镜专家的敏感度分别为92.08%和91.06%,特异度分别为95.58%和92.79%,CNN与2名内镜专家的诊断结果比较差异均无统计学意义(P=0.282,P=0.085)。此外，CNN对早期鼻咽癌的识别准确率与晚期鼻咽癌比较，差异无统计学意义(P=0.382)。测试集图像识别时间为0.1 s/张。结论:CNN模型能快速区别鼻咽癌和鼻咽良性病变，有助于内镜医生判断鼻咽病变，减少鼻咽部活检率。更多还原

【Abstract】 Objective: To evaluate the diagnostic accuracy of the convolutional neural network(CNN) in diagnosing nasopharyngeal carcinoma using endoscopic narrowband imaging. Methods: A total of 834 cases with nasopharyngeal lesions were collected from the People’s Hospital of Guangxi Zhuang Autonomous Region between 2014 and 2016. We trained the DenseNet201 model to classify the endoscopic images, evaluated its performance using the test dataset, and compared the results with those of two independent endoscopic experts. Results: The area under the ROC curve of the CNN in diagnosing nasopharyngeal carcinoma was 0.98. The sensitivity and specificity of the CNN were 91.90% and 94.69%, respectively. The sensitivity of the two expert-based assessment was 92.08% and 91.06%, respectively, and the specificity was 95.58% and 92.79%, respectively. There was no significant difference between the diagnostic accuracy of CNN and the expert-based assessment(P=0.282, P=0.085). Moreover, there was no significant difference in the accuracy in discriminating early-stage and late-stage nasopharyngeal carcinoma(P=0.382). The CNN model could rapidly distinguish nasopharyngeal carcinoma from benign lesions, with an image recognition time of 0.1 s/piece. Conclusion: The CNN model can quickly distinguish nasopharyngeal carcinoma from benign nasopharyngeal lesions, which can aid endoscopists in diagnosing nasopharyngeal lesions and reduce the rate of nasopharyngeal biopsy.更多还原