Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)
Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)
复制标题
利用深度学习系统在内窥镜检查中筛查早期食管鳞状细胞癌（附视频）
DOI：
10.1016/j.gie.2019.06.044
复制
发表时间：
2019-11-01
影响因子：
7.7
通讯作者：
Zhong, Yun-Shi
中科院分区：
文献类型：
--
作者：
Cai, Shi-Lun;Li, Bing;Zhong, Yun-Shi
背景和目标：很少有基于人工智能的技术被开发出来以提高食管鳞状细胞癌（ESCC）的筛查效率。在这里，我们开发并验证了一种新的计算机辅助检测（CAD）系统，该系统使用深度神经网络（DNN）在传统内窥镜白光成像下定位和识别早期ESCC。我们收集了2428（1332异常，746例患者的1096例正常食管镜图像，以建立一种新的DNN-CAD系统，并准备了包含52名患者的187张图像的验证数据集。要求16名内窥镜医生（高级、中级和初级）审查确认集的图像。结果：DNN-CAD的受试者工作特征曲线显示，DNN-CAD的曲线下面积> 96%，DNN-CAD的阳性预测值（PPV）和阴性预测值（NPV）均大于96%。对于验证数据集，DNN-CAD的灵敏度、特异性、准确性、PPV和NPV分别为97.8%、85.4%、91.4%、86.4%和97.6%。老年组的平均诊断准确率为88.8%，而青年组的诊断准确率较低，为77.2%。参考DNN-CAD结果后，内镜医师的平均诊断能力提高，尤其是在灵敏度方面（74.2% vs 89.2%），准确性（81.7% vs 91.1%）和NPV（79.3%对90.4%）。用于早期食管鳞癌筛查的新型DNN-CAD系统具有较高的准确性和灵敏度，并且可以帮助内窥镜医师检测以前在白光成像下被忽略的病变。
Background and Aims: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging.Methods: We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists.Results: The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%).Conclusions: The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.