Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China; Endoscopy Research Institute of Fudan University, Shanghai, China.
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.
Gastrointest Endosc. 2019 Nov;90(5):745-753.e2. doi: 10.1016/j.gie.2019.06.044. Epub 2019 Jul 11.
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.
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.
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%).
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.
目前开发的人工智能技术很少用于提高食管鳞状细胞癌（ESCC）筛查的效率。在这里，我们开发并验证了一种使用深度神经网络（DNN）在常规内镜白光成像下定位和识别早期 ESCC 的新型计算机辅助检测（CAD）系统。
我们在 2 个中心收集了 746 名患者的 2428 张（1332 张异常，1096 张正常）食管内窥镜图像，建立了一种新型的 DNN-CAD 系统，并准备了一个包含 52 名患者的 187 张图像的验证数据集。16 名内镜医生（高级、中级和初级）被要求检查验证集的图像。比较 DNN-CAD 系统和内镜医生的诊断结果，包括准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。
DNN-CAD 的受试者工作特征曲线表明，曲线下面积>96%。对于验证数据集，DNN-CAD 的敏感性、特异性、准确性、PPV 和 NPV 分别为 97.8%、85.4%、91.4%、86.4%和 97.6%。高级组的平均诊断准确率为 88.8%，而初级组的诊断准确率较低，为 77.2%。在参考 DNN-CAD 的结果后，内镜医生的平均诊断能力得到了提高，特别是在敏感性（74.2%对 89.2%）、准确性（81.7%对 91.1%）和 NPV（79.3%对 90.4%）方面。
用于筛查早期 ESCC 的新型 DNN-CAD 系统具有较高的准确性和敏感性，可帮助内镜医生检测白光成像下先前忽略的病变。
