Yao Liwen, Zhang Jun, Liu Jun, Zhu Liangru, Ding Xiangwu, Chen Di, Wu Huiling, Lu Zihua, Zhou Wei, Zhang Lihui, Xu Bo, Hu Shan, Zheng Biqing, Yang Yanning, Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Gastroenterology, Wuhan Union Hospital, Huazhong University of Science and Technology, Wuhan, China.
EBioMedicine. 2021 Mar;65:103238. doi: 10.1016/j.ebiom.2021.103238. Epub 2021 Feb 24.
Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for EUS BD scanning augmentation.
The scanning was divided into 4 stations. We developed a station classification model and a BD segmentation model with 10681 images and 2529 images, respectively. 1704 images and 667 images were applied to classification and segmentation internal validation. For classification and segmentation video validation, 264 and 517 videos clips were used. For man-machine contest, an independent data set contained 120 images was applied. 799 images from other two hospitals were used for external validation. A crossover study was conducted to evaluate the system effect on reducing difficulty in ultrasound images interpretation.
For classification, the model achieved an accuracy of 93.3% in image set and 90.1% in video set. For segmentation, the model had a dice of 0.77 in image set, sensitivity of 89.48% and specificity of 82.3% in video set. For external validation, the model achieved 82.6% accuracy in classification. In man-machine contest, the models achieved 88.3% accuracy in classification and 0.72 dice in BD segmentation, which is comparable to that of expert. In the crossover study, trainees' accuracy improved from 60.8% to 76.3% (P < 0.01, 95% C.I. 20.9-27.2).
We developed a deep learning-based augmentation system for EUS BD scanning augmentation.
Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Hubei Province Major Science and Technology Innovation Project, National Natural Science Foundation of China.
在超声内镜（EUS）检查过程中，胆管（BD）的详细评估是主要关注点。本研究的目的是开发一种用于增强EUS胆管扫描的系统。
扫描分为4个部位。我们分别用10681张图像和2529张图像开发了一个部位分类模型和一个胆管分割模型。1704张图像和667张图像用于分类和分割内部验证。对于分类和分割视频验证，使用了264个和517个视频片段。对于人机竞赛，应用了一个包含120张图像的独立数据集。来自其他两家医院的799张图像用于外部验证。进行了一项交叉研究，以评估该系统对降低超声图像解读难度的效果。
对于分类，该模型在图像集上的准确率为93.3%，在视频集上为90.1%。对于分割，该模型在图像集上的骰子系数为0.77，在视频集上的灵敏度为89.48%，特异性为82.3%。对于外部验证，该模型在分类上的准确率为82.6%。在人机竞赛中，该模型在分类上的准确率为88.3%，在胆管分割上的骰子系数为0.72，与专家的水平相当。在交叉研究中，受训者的准确率从60.8%提高到了76.3%（P<0.01，95%置信区间20.9 - 27.2）。
我们开发了一种基于深度学习的增强系统，用于增强EUS胆管扫描。
湖北省消化疾病微创切口临床研究中心、湖北省重大科技创新项目、国家自然科学基金。
