The purpose of this study was to develop and evaluate the performance of deep learning methods based on convolutional neural networks (CNN) to detect and identify specific hip arthroplasty models. In this study, we propose a novel deep learning-based approach to identify hip arthroplasty implants’ design using anterior–posterior images of both the stem and the cup. We harness the pre-trained ResNet50 CNN model and employ transfer learning methods to adapt the model for the implants identification task using a total of 714 radiographs of 4 different hip arthroplasty implant designs. Performance was compared with the operative notes and crosschecked with implant sheets. We also evaluate the difference in performance of models trained with the images of the stem, the cup or both. The training and validation data sets were comprised of 357 stem images and 357 cup radiographs across 313 patients and included 4 hip arthroplasty implants from 4 leading implant manufacturers. After 1000 training epochs the model classified 4 implant models with very high accuracy. Our results showed that jointly using stem images and cup images did not improve the classification accuracy of the CNN model. CNN can accurately distinguish between specific hip arthroplasty designs. This technology could offer a useful adjunct to the surgeon in preoperative identification of the prior implant. Using stem images or cup images to train the CNN can both achieve effective identification accuracy, with the accuracy of the stem images being higher. Using stem images and cup images together is not more effective than using images from only one perspective.

本研究的目的是开发和评估基于卷积神经网络 (CNN) 的深度学习方法的性能，以检测和识别特定的髋关节置换模型。在这项研究中，我们提出了一种新的基于深度学习的方法，使用柄和杯的前后图像来识别髋关节置换术植入物的设计。我们利用预训练的 ResNet50 CNN 模型并采用迁移学习方法，使用 4 种不同髋关节置换植入物设计的总共 714 张射线照片来调整模型以适应植入物识别任务。将性能与手术记录进行比较，并与种植体表进行交叉检查。我们还评估了使用茎、杯子或两者图像训练的模型的性能差异。训练和验证数据集由 313 名患者的 357 张干图像和 357 张杯形 X 光片组成，包括来自 4 家领先植入物制造商的 4 种髋关节置换术植入物。在 1000 个训练 epoch 之后，该模型以非常高的准确度分类了 4 个种植体模型。我们的结果表明，联合使用茎图像和杯子图像并没有提高 CNN 模型的分类精度。CNN 可以准确区分特定的髋关节置换术设计。该技术可以为外科医生在术前识别先前植入物方面提供有用的辅助手段。使用茎图像或杯子图像训练 CNN 都可以达到有效的识别精度，茎图像的准确度更高。一起使用茎图像和杯子图像并不比仅使用一个角度的图像更有效。