Endoscopic detection and differentiation of esophageal lesions using a deep neural network
Endoscopic detection and differentiation of esophageal lesions using a deep neural network
复制标题
DOI：
10.1016/j.gie.2019.09.034
复制
发表时间：
2020-02-01
影响因子：
7.7
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
--
作者：
Ohmori, Masayasu;Ishihara, Ryu;Tada, Tomohiro
背景和目的：食道鳞状细胞癌(SCC)的诊断依赖于医生个人的专业知识，可能会受到观察者之间的差异。方法：用9591张非放大内窥镜(Non-ME)和7844张经病理证实的浅表食管鳞癌的ME图像和1692张非ME图像和3435张ME图像作为训练图像数据。使用来自135名患者的255张非ME白光图像、268张非ME窄带图像/蓝激光图像和204张ME窄带图像/蓝激光图像进行验证。结果：对于窄带成像/蓝激光成像的非ME诊断，人工智能(AI)系统的灵敏度、特异度和准确率分别为100%、63%和77%，经验丰富的内窥镜医师的灵敏度、特异度和准确率分别为92%、69%和78%。对于白光成像的非ME诊断，AI系统的敏感性、特异性和准确性分别为90%、76%和81%，经验丰富的内窥镜医生的敏感性、特异性和准确性分别为87%、67%和75%。对于ME诊断，AI系统的敏感度、特异度和准确率分别为98%、56%和77%，经验丰富的内窥镜医师的敏感度、特异度和准确率分别为83%、70%和76%。结论：我们的人工智能系统对非ME检测鳞癌具有较高的灵敏度，ME对鳞癌与非癌病变的鉴别诊断具有较高的准确性。
Background and Aims: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentiate esophageal SCC.Methods: A total of 9591 nonmagnified endoscopy (non-ME) and 7844 ME images of pathologically confirmed superficial esophageal SCCs and 1692 non-ME and 3435 ME images from noncancerous lesions or normal esophagus were used as training image data. Validation was performed using 255 non-ME white-light images, 268 non-ME narrow-band images/blue-laser images, and 204 ME narrow-band images/blue-laser images from 135 patients. The same validation test data were diagnosed by 15 board-certified specialists (experienced endoscopists).Results: Regarding diagnosis by non-ME with narrow-band imaging/blue-laser imaging, the sensitivity, specificity, and accuracy were 100%, 63%, and 77%, respectively, for the artificial intelligence (AI) system and 92%, 69%, and 78%, respectively, for the experienced endoscopists. Regarding diagnosis by non-ME with white-light imaging, the sensitivity, specificity, and accuracy were 90%, 76%, and 81%, respectively, for the AI system and 87%, 67%, and 75%, respectively, for the experienced endoscopists. Regarding diagnosis by ME, the sensitivity, specificity, and accuracy were 98%, 56%, and 77%, respectively, for the AI system and 83%, 70%, and 76%, respectively, for the experienced endoscopists. There was no significant difference in the diagnostic performance between the AI system and the experienced endoscopists.Conclusions: Our AI system showed high sensitivity for detecting SCC by non-ME and high accuracy for differentiating SCC from noncancerous lesions by ME.