食管胃十二指肠镜检查
医学
盲点
内窥镜检查
不利影响
内窥镜
外科
内科学
人工智能
计算机科学
作者
Lianlian Wu,Jun Zhang,Wei Zhou,Ping An,Lei Shen,Jun Liu,Xiaoda Jiang,Xu Huang,Ganggang Mu,Xinyue Wan,Xiaoguang Lv,Juan Gao,Ning Cui,Shan Hu,Yiyun Chen,Xiao Hu,Jiangjie Li,Di Chen,Dexin Gong,Xinqi He
出处
期刊:Gut
[BMJ]
日期:2019-03-11
卷期号:68 (12): 2161-2169
被引量:298
标识
DOI:10.1136/gutjnl-2018-317366
摘要
Objective Esophagogastroduodenoscopy (EGD) is the pivotal procedure in the diagnosis of upper gastrointestinal lesions. However, there are significant variations in EGD performance among endoscopists, impairing the discovery rate of gastric cancers and precursor lesions. The aim of this study was to construct a real-time quality improving system, WISENSE, to monitor blind spots, time the procedure and automatically generate photodocumentation during EGD and thus raise the quality of everyday endoscopy. Design WISENSE system was developed using the methods of deep convolutional neural networks and deep reinforcement learning. Patients referred because of health examination, symptoms, surveillance were recruited from Renmin hospital of Wuhan University. Enrolled patients were randomly assigned to groups that underwent EGD with or without the assistance of WISENSE. The primary end point was to ascertain if there was a difference in the rate of blind spots between WISENSE-assisted group and the control group. Results WISENSE monitored blind spots with an accuracy of 90.40% in real EGD videos. A total of 324 patients were recruited and randomised. 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001), and the mean difference was −15.39% (95% CI −19.23 to −11.54). There was no significant adverse event. Conclusions WISENSE significantly reduced blind spot rate of EGD procedure and could be used to improve the quality of everyday endoscopy. Trial registration number ChiCTR1800014809; Results.
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