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利用深度学习确定膝关节 X 光片中的解剖部位。

Determining the anatomical site in knee radiographs using deep learning.

机构信息

Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.

出版信息

Sci Rep. 2022 Mar 7;12(1):3995. doi: 10.1038/s41598-022-08020-7.

Abstract

An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior-posterior direction. In this retrospective study, a ResNet-34 network was trained on 2892 radiographs from 2540 patients to predict the anatomical side of knees in radiographs. The network was evaluated in an internal validation cohort of 932 radiographs of 816 patients and in an external validation cohort of 490 radiographs from 462 patients. The network showed an accuracy of 99.8% and 99.9% on the internal and external validation cohort, respectively, which is comparable to the accuracy of radiographers. Anatomical side in radiographs of the knee in anterior-posterior direction can be deduced from radiographs with high accuracy using deep learning.

摘要

X 射线的一个重要质量标准是正确的解剖侧标记。评估了一个深度神经网络,以预测在前后方向获取的膝关节 X 射线片中正确的解剖侧。在这项回顾性研究中,使用来自 2540 名患者的 2892 张 X 射线片对 ResNet-34 网络进行了训练,以预测 X 射线片中膝关节的解剖侧。该网络在内部验证队列(816 名患者的 932 张 X 射线片)和外部验证队列(462 名患者的 490 张 X 射线片)中进行了评估。该网络在内部验证组和外部验证组中的准确率分别为 99.8%和 99.9%,与放射技师的准确率相当。使用深度学习可以从前后方向的膝关节 X 射线片中非常准确地推断出解剖侧。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c86/8901647/78588d30616a/41598_2022_8020_Fig1_HTML.jpg

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