Deep Learning-based Automated Identification of Knee Arthroplasty Implants

Abstract Background: A critical step in preoperative planning for revision knee arthroplasty is the identification of the failed implant. 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 knee arthroplasty models. Methods: In this study, we propose a novel deep learning-based approach to identify knee arthroplasty implants’ design using both anterior-posterior (AP) and lateral images. 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 1814 radiographs of 8 different knee 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 AP images, lateral images or both. Results: The training and validation data sets were comprised of 907 AP and 907 lateral view radiographs across 733 patients and included 8 knee arthroplasty implants from 5 leading implant manufactures. After 1000 training epochs the model classified 8 implant models with very high accuracy. Our results showed that jointly using AP images and lateral images improved the classification accuracy of the CNN model. Conclusion: CNN can accurately distinguish between specific knee arthroplasty designs. This technology could offer a useful adjunct to the surgeon in preoperative identification of the prior implant. Furthermore, using both AP images and lateral images to train the CNN is more effective than using images from only one perspective.