Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: A multicenter retrospective study

金标准(测试) 卷积神经网络 医学 内镜超声检查 人工智能 放射科 试验装置 核医学 计算机科学 模式识别(心理学) 内窥镜检查
作者
Shuxin Tian,Huiying Shi,Weigang Chen,Shijie Li,Chaoqun Han,Fei Du,Weijun Wang,Hao Wen,Yali Lei,Liang Deng,Jing Tang,Jinjie Zhang,Jing Lin,Lei Shi,Bo Ning,Kui Zhao,Jiarong Miao,Guobao Wang,Hui Huang,Xiaoxi Huang,Wenjie Kong,Xiaojuan Jin,Ding Zhang,Rui‐Biao Lin
出处
期刊:International Journal of Surgery [Elsevier]
卷期号:110 (3): 1637-1644
标识
DOI:10.1097/js9.0000000000000995
摘要

There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images.The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared.In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98).The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.
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