Abstract：
BACKGROUND: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors.
METHODS: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions.
RESULTS: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models.
CONCLUSIONS: The proposed model is efficient and can minimize the revision complexities of implants.
METHODS: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions.
RESULTS: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models.
CONCLUSIONS: The proposed model is efficient and can minimize the revision complexities of implants.
摘要：
背景：再次手术前早期识别假体可以降低围手术期的发病率和死亡率。由于肩部生物力学的复杂性，术前准确分类植入物模型是制定正确医疗程序和设置个性化医疗设备的基础。专家外科医生通常使用假体的X射线图像来设置患者特定的设备。然而,这种主观方法耗时且容易出错。
方法：作为替代方案，人工智能在骨科手术和临床决策中对假体的准确放置起到了至关重要的作用。在这项研究中,提出了三种不同的基于深度学习的框架来识别X射线扫描中不同类型的肩部植入物。我们主要提出了一种高效的集成网络，称为初始移动全连接卷积网络(IMFC-Net)，它由我们设计的两个卷积神经网络和一个分类器组成。为了评估IMFC-Net和最先进的模型的性能，实验使用597名去识别患者(597例肩部植入物)的公开数据集进行.此外，为了证明IMFC-Net的通用性，实验是用两种增强技术进行的，没有增强，我们的模型排名第一，与比较模型有相当大的差异。还使用梯度加权类激活图技术来找到IMFC-Net分类决策所需的不同植入物特征。
结果：结果证实，提出的IMFC-Net模型的平均精度为89.09％，准确率为89.54%，召回率为86.57%，还有一个F1。得分为87.94%，高于比较模型。
结论：所提出的模型是有效的，并且可以最大程度地减少植入物的翻修复杂性。
方法：作为替代方案，人工智能在骨科手术和临床决策中对假体的准确放置起到了至关重要的作用。在这项研究中,提出了三种不同的基于深度学习的框架来识别X射线扫描中不同类型的肩部植入物。我们主要提出了一种高效的集成网络，称为初始移动全连接卷积网络(IMFC-Net)，它由我们设计的两个卷积神经网络和一个分类器组成。为了评估IMFC-Net和最先进的模型的性能，实验使用597名去识别患者(597例肩部植入物)的公开数据集进行.此外，为了证明IMFC-Net的通用性，实验是用两种增强技术进行的，没有增强，我们的模型排名第一，与比较模型有相当大的差异。还使用梯度加权类激活图技术来找到IMFC-Net分类决策所需的不同植入物特征。
结果：结果证实，提出的IMFC-Net模型的平均精度为89.09％，准确率为89.54%，召回率为86.57%，还有一个F1。得分为87.94%，高于比较模型。
结论：所提出的模型是有效的，并且可以最大程度地减少植入物的翻修复杂性。
