Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models.
Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior–posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization.
After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps.
Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.
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Borjali, A., Chen, A. F., Muratoglu, O. K., Morid, M. A., & Varadarajan, K. M. (2020). Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. Journal of Orthopaedic Research. https://doi.org/10.1002/jor.24617
Bredow, J., Wenk, B., Westphal, R., Wahl, F., Budde, S., Eysel, P., et al. (2014). Software-based matching of X-ray images and 3D models of knee prostheses. Technology and Health Care. https://doi.org/10.3233/THC-140858
Wilson, N. A., Jehn, M., York, S., & Davis, C. M. (2014). Revision total hip and knee arthroplasty implant identification: Implications for use of unique device identification 2012 AAHKS member survey results. Journal of Arthroplasty. https://doi.org/10.1016/j.arth.2013.06.027
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In Proceedings—30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017. https://doi.org/10.1109/CVPR.2017.243
Wilson, N., Broatch, J., Jehn, M., & Davis, C. (2015). National projections of time, cost and failure in implantable device identification: Consideration of unique device identification use. Healthcare. https://doi.org/10.1016/j.hjdsi.2015.04.003
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.90
Kang, Y. J., Yoo, J. I., Cha, Y. H., Park, C. H., & Kim, J. T. (2020). Machine learning-based identification of hip arthroplasty designs. Journal of Orthopaedic Translation. https://doi.org/10.1016/j.jot.2019.11.004
Karnuta, J. M., Luu, B. C., Roth, A. L., Haeberle, H. S., Chen, A. F., Iorio, R., et al. (2020). Artificial intelligence to identify arthroplasty implants from radiographs of the knee. Journal of Arthroplasty. https://doi.org/10.1016/j.arth.2020.10.021
Urban, G., Porhemmat, S., Stark, M., Feeley, B., Okada, K., & Baldi, P. (2020). Classifying shoulder implants in X-ray images using deep learning. Computational and Structural Biotechnology Journal, 18, 967–972. https://doi.org/10.1016/j.csbj.2020.04.005
Chow, L. S., & Rajagopal, H. (2017). Modified-BRISQUE as no reference image quality assessment for structural MR images. Magnetic Resonance Imaging. https://doi.org/10.1016/j.mri.2017.07.016
Howard, J. P., Fisher, L., Shun-Shin, M. J., Keene, D., Arnold, A. D., Ahmad, Y., et al. (2019). Cardiac rhythm device identification using neural networks. JACC Clinical Electrophysiology. https://doi.org/10.1016/j.jacep.2019.02.003
Dy, C. J., Bozic, K. J., Padgett, D. E., Pan, T. J., Marx, R. G., & Lyman, S. (2014). Is changing hospitals for revision total joint arthroplasty associated with more complications? Clinical Orthopaedics and Related Research. https://doi.org/10.1007/s11999-014-3515-z
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00474
Lu H, Wang M. RL4health: Crowdsourcing reinforcement learning for knee replacement pathway optimization. ArXiv 2019.
Deng, J., Dong, W., Socher, R., Li, L.-J., Kai, L., & Li, F.-F. (2010). ImageNet: A large-scale hierarchical image database. 2010. https://doi.org/10.1109/cvpr.2009.5206848.
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Sharma, S., Batta, V., Chidambaranathan, M. et al. Knee Implant Identification by Fine-Tuning Deep Learning Models. JOIO (2021). https://doi.org/10.1007/s43465-021-00529-9
- Knee implant
- Revision arthroplasty
- Implant identification
- Deep learning
- Image processing