Xu Zhang, Tao Yu, Wenfang Zheng, Ne Lin, Zhengxing Huang, Jiquan Liu, Weiling Hu, Huilong Duan, Jianmin Si
Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, People's Republic of China.
Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, 310016, People's Republic of China.
Healthc Technol Lett. 2019 Nov 26;6(6):176-180. doi: 10.1049/htl.2019.0066. eCollection 2019 Dec.
Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors' model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.
食管胃十二指肠镜检查（EGD）已广泛应用于胃肠道（GI）检查。然而，目前缺乏成熟的技术来评估EGD检查过程的质量。在这篇信函中，作者设计了一种多任务解剖结构检测卷积神经网络（MT-AD-CNN），通过将上消化道十个解剖结构的检测任务与信息性视频帧的分类任务相结合，来评估EGD检查质量。作者的模型能够消除胃镜视频中的非信息性帧，并实时检测解剖结构。具体而言，在检测网络中添加了一个子分支，用于对窄带成像（NBI）图像、信息性图像和非信息性图像进行分类。这样，检测框将仅显示在信息性帧上，从而可以降低误报率。他们能够确定每个解剖位置得到有效检查的视频帧，进而分析诊断质量。他们的方法在检测任务上达到了93.74%的平均精度均值性能，在分类任务上达到了98.77%的准确率。他们的模型能够反映胃镜检查过程的详细情况，在提高检查质量方面显示出应用潜力。
