Automated Identification of Hip Arthroplasty Implants Using Artificial Intelligence

Objectives: 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. Methods: : In this study, we propose a novel deep learning-based approach to identify hip arthroplasty implants’ design using anterior-posterior (AP) 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 implants identification task using a total of 714 radiographs of 4 different hip arthroplasty implant designs. Performance was compared with operative note and crosschecked with implant sheets. We also evaluate the difference of performance of models trained with the images of the stem, the cup or both. Result: 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 manufactures. 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. Conclusion: 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. However, using both stem images and cup images to train the CNN is not more effective than using images from only one perspective.