Classifying shoulder implants in X-ray images using deep learning
Classifying shoulder implants in X-ray images using deep learning
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DOI：
10.1016/j.csbj.2020.04.005
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发表时间：
2020-01-01
影响因子：
6
通讯作者：
Baldi, Pierre
中科院分区：
文献类型：
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作者：
Urban, Gregor;Porhemmat, Saman;Baldi, Pierre
全肩关节置换术(TSA)是一种用假体替换肩部受损球体的手术。许多年后，这个假体可能需要维修或更换。在某些情况下，例如当患者改变居住国家时，患者和主治医生可能不知道假体的型号和制造商。为了选择正确的设备和程序，需要在手术前正确识别植入物的型号。我们提出了一种新的方法来自动分类X光图像中的肩部植入物。我们使用深度学习模型，并将它们的性能与其他分类器进行比较，例如随机森林和梯度提升。我们发现，当且仅当使用像ImageNet这样的域外数据来预训练模型时，深度卷积神经网络的性能显著优于其他分类器。在一个包含来自4个制造商和16个不同型号的肩部植入物的X射线图像的数据集中，深度学习能够在10次交叉验证中以大约80%的准确率识别正确的制造商，而其他分类器的准确率为56%或更低。我们相信，这种方法在临床实践中将是一种有用的工具，并可能适用于其他类型的假体。(C)2020作者。由Elsevier B.V.代表计算和结构生物技术研究网络出版。
Total Shoulder Arthroplasty (TSA) is a type of surgery in which the damaged ball of the shoulder is replaced with a prosthesis. Many years later, this prosthesis may be in need of servicing or replacement. In some situations, such as when the patient has changed his country of residence, the model and the manufacturer of the prosthesis may be unknown to the patient and primary doctor. Correct identification of the implant's model prior to surgery is required for selecting the correct equipment and procedure. We present a novel way to automatically classify shoulder implants in X-ray images. We employ deep learning models and compare their performance to alternative classifiers, such as random forests and gradient boosting. We find that deep convolutional neural networks outperform other classifiers significantly if and only if out-of-domain data such as ImageNet is used to pre-train the models. In a data set containing X-ray images of shoulder implants from 4 manufacturers and 16 different models, deep learning is able to identify the correct manufacturer with an accuracy of approximately 80% in 10-fold cross validation, while other classifiers achieve an accuracy of 56% or less. We believe that this approach will be a useful tool in clinical practice, and is likely applicable to other kinds of prostheses. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.