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Abstract：
Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient\'s anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.
摘要：
再次手术和修正通常在经历了全肩关节置换术（TSA）和反向全肩关节置换术（RTSA）的患者中进行。这需要植入物模型和制造商的准确识别,以根据患者的解剖结构设置正确的设备和程序作为个性化医疗。由于患者医疗数据的不可用性和模糊性，专业外科医生通过X线图像的视觉比较来识别植入物。错误的步骤会导致粗心，发病率，额外的货币权重，浪费时间.尽管模式识别和深度学习在医学领域取得了重大进展，关于肩部植入物分类的研究非常有限。为了克服这些问题,我们提出了一个强大的基于深度学习的框架，该框架由卷积神经网络（CNN）的集合组成，用于对不同患者的X射线图像中的肩关节植入物进行分类。通过我们的旋转不变增强，训练数据集的大小增加了36倍。然后将修改后的ResNet和DenseNet深度组合以形成密集的残差集合网络（DRE-Net）。要评估DRE-Net，实验是在公开可用的肩部植入物X射线数据集上进行的10倍交叉验证。实验结果表明,DRE-Net取得了较好的精度,F1分数，精度,召回率85.92%，84.69%,85.33%,和84.11%，分别,高于最先进的方法。此外，我们通过在开放世界配置中测试网络来确认网络的泛化能力，以及旋转不变增强的有效性。
