Abstract：
BACKGROUND: The anticipated growth of total hip arthroplasty will result in an increased need for revision total hip arthroplasty. Preoperative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution.
METHODS: We trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy.
RESULTS: Our machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%.
CONCLUSIONS: Our novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.
METHODS: We trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy.
RESULTS: Our machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%.
CONCLUSIONS: Our novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.
摘要：
背景：全髋关节置换术的预期增长将导致翻修全髋关节置换术的需求增加。术前规划,包括识别当前的植入物，对于成功的翻修手术至关重要。人工智能(AI)有望帮助临床决策，包括髋关节植入物识别。然而,以前的研究有局限性，如小数据集，不同的茎设计，有限的可扩展性，以及对AI专业知识的需求。为了解决这些限制，我们开发了一种新的技术来生成大数据集，射线检查相似的茎，并展示了利用无代码机器学习解决方案的可扩展性。
方法：我们训练，已验证，并测试了自动机器学习实现的卷积神经网络，以对9个射线照相相似的股骨植入物进行分类，并采用干phy端拟合楔形锥度设计。我们的新技术使用在计算机断层扫描骨盆体积内叠加的3维扫描植入物模型的计算机断层扫描衍生投影。我们使用计算机辅助设计建模和MATLAB来处理和操纵图像。这产生了27,020个图像用于训练(22,957)和验证(4,063)集。我们从各种来源获得了786张测试图像。通过计算灵敏度来评估模型的性能，特异性，和准确性。
结果：我们的机器学习模型区分了9个植入模型，平均准确率为97.4%，灵敏度为88.4%，特异性为98.5%。
结论：我们的新型髋关节植入物检测技术准确地识别了9个射线照相相似的植入物。该方法生成大型数据集，是可扩展的，并可能包括历史或晦涩的植入物。无代码机器学习模型证明了在没有人工智能专业知识的情况下获得有意义的结果的可行性。鼓励这方面的进一步研究。
方法：我们训练，已验证，并测试了自动机器学习实现的卷积神经网络，以对9个射线照相相似的股骨植入物进行分类，并采用干phy端拟合楔形锥度设计。我们的新技术使用在计算机断层扫描骨盆体积内叠加的3维扫描植入物模型的计算机断层扫描衍生投影。我们使用计算机辅助设计建模和MATLAB来处理和操纵图像。这产生了27,020个图像用于训练(22,957)和验证(4,063)集。我们从各种来源获得了786张测试图像。通过计算灵敏度来评估模型的性能，特异性，和准确性。
结果：我们的机器学习模型区分了9个植入模型，平均准确率为97.4%，灵敏度为88.4%，特异性为98.5%。
结论：我们的新型髋关节植入物检测技术准确地识别了9个射线照相相似的植入物。该方法生成大型数据集，是可扩展的，并可能包括历史或晦涩的植入物。无代码机器学习模型证明了在没有人工智能专业知识的情况下获得有意义的结果的可行性。鼓励这方面的进一步研究。
