基于深度学习的消化内镜检查辅助质量控制系统研究（含视频）

目的构建智能消化内镜质控系统并评估其在胃肠镜检查中的质量监控作用。方法基于医学数字成像与通信协议，获取武汉大学人民医院消化内镜中心2016年12月—2018年10月胃肠镜检查患者的电子医疗记录和图像，采用深度卷积神经网络和深度强化学习方法开发智能消化内镜质控系统。该系统运用回盲部识别模型、体内外图像识别模型以及胃的26个部位识别模型，监控达盲率、肠镜退镜时间、胃镜检查时间、胃镜检查覆盖部位数等质控指标。随机选取武汉大学人民医院消化内镜中心2019年3—11月83例胃镜检查和205例肠镜检查患者的图像,测试智能消化内镜质控系统质量控制功能的准确性。结果智能消化内镜质控系统由胃镜质量分析、肠镜质量分析组成，可随时自动生成包含各质控指标的内镜医师胃肠镜检查质控报告。该系统监控的达盲率、肠镜退镜时间、胃镜检查时间和胃镜检查覆盖部位数的准确率分别为92.5%（172/186）、91.7%（188/205）、100.0%（83/83）和89.3%（1 928/2 158）。结论智能消化内镜质控系统可实现胃肠镜检查的质量监控作用，以便内镜医师了解自身的工作情况，从而提升胃肠镜检查质量。

Evaluation of performance measurement system of gastrointestinal endoscopy based on deep learning (with video)

ObjectiveTo construct an intelligent performance measurement system of gastrointestinal endoscopy and to analyze its value for endoscopic quality improvement. MethodsThe intelligent gastrointestinal endoscopy performance measurement system was developed by using the deep convolutional neural network (DCNN) and deep reinforcement learning, based on the Digital Imaging and Communications in Medicine. Images were acquired of patients undergoing gastrointestinal endoscopy at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from December 2016 to October 2018. The system applied cecum recognition model (DCNN1), images in vitro and in vivo recognition model (DCNN2), and identification model at 26 gastric sites (DCNN3) to monitor indices such as cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage. Images of 83 gastroscopies and 205 colonoscopies acquired at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March to November 2019 were randomly selected to examine the effectiveness of the system. ResultsThe intelligent gastrointestinal endoscopy performance measurement system consisted of quality analysis of both gastroscopy and colonoscopy, including all indices, and could be generated automatically at any time. The accuracy for cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage were 92.5% (172/186), 91.7% (188/205), 100.0% (83/83), 89.3% (1 928/2 158), respectively. ConclusionThe intelligent performance measurement system for gastrointestinal endoscopy can be recommended for the quality control of gastrointestinal endoscopy, from which endoscopists can get feedback and improve the quality of gastrointestinal endoscopy.