Abstract
Total knee arthroplasty (TKA) is one of the most successful surgical procedures worldwide. It improves quality of life, mobility, and functionality for the vast majority of patients. However, a TKA surgery may fail over time for several reasons, thus it requires a revision arthroplasty surgery. Identifying TKA implants is a critical consideration in preoperative planning of revision surgery. This study aims to develop, train, and validate deep convolutional neural network models to precisely classify four widely-used TKA implants based on only plain knee radiographs. Using 9,052 computationally annotated knee radiographs, we achieved weighted average precision, recall, and F1-score of 0.97, 0.97, and 0.97, respectively, with Cohen Kappa of 0.96.
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References
Kurtz, S.M., Ong, K.L., Lau, E., Bozic, K.J.: Impact of the economic downturn on total joint replacement demand in the United States: updated projections to 2021. JBJS 96(8), 624–30 (2014)
Kremers, H.M., et al.: Prevalence of total hip and knee replacement in the United States. J. Bone Joint Surg. Am. Vol. 97(17), 1386 (2015)
Losina, E., et al.: Cost-effectiveness of total knee arthroplasty in the United States: patient risk and hospital volume. Arch. Intern. Med. 169(12), 1113–21 (2009)
Kremers, H.M., et al.: Comparative survivorship of different tibial designs in primary total knee arthroplasty. J. Bone Joint Surg. Am. 96(14), e121 (2014)
Price, A.J., et al.: Knee replacement. Lancet 392(10158), 1672–1682 (2018)
Bozic, K.J., et al.: The epidemiology of revision total knee arthroplasty in the United States. Clin. Orthop. Relat. Res.® 468(1), 45–51 (2010)
Kurtz, S., Ong, K., Lau, E., Mowat, F., Halpern, M.: Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. Jbjs 89(4), 780–5 (2007)
Wilson, N.A., Jehn, M., York, S., Davis III, C.M.: Revision total hip and knee arthroplasty implant identification: implications for use of unique device identification 2012 AAHKS member survey results. J. Arthroplasty 29(2), 251–5 (2014)
Steiner, C., Andrews, R., Barrett, M., Weiss, A.: HCUP projections: mobility/orthopedic procedures 2003 to 2012. US agency for healthcare research and quality (2012)
Scuderi, G.R.: Revision total knee arthroplasty: how much constraint is enough? Clin. Orthop. Relat. Res.® 392, 300–305 (2001)
Paul, H.Y., et al.: Automated detection & classification of knee arthroplasty using deep learning. Knee 27, 535–542 (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480 (2017)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 23 November 2015
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence, 12 February 2017
Strubell, E., Verga, P., Belanger, D., McCallum, A.: Fast and accurate entity recognition with iterated dilated convolutions. arXiv preprint arXiv:1702.02098, 7 February 2017
Zhou, L., Zhang, C., Wu, M.: D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: CVPR Workshops, 18 June 2018, pp. 182–186 (2018)
Wang, Y., Hu, S., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for crowd counting. Multimed. Tools Appl. 79, 1057–1073 (2019). https://doi.org/10.1007/s11042-019-08208-6
Liu, S., Xu, H., Liu, Y., Xie, H.: Improving brain tumor segmentation with dilated pseudo-3D convolution and multi-direction fusion. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11961, pp. 727–738. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37731-1_59
Wang, B., Zhang, X., Zhou, X., Li, J.: A gated dilated convolution with attention model for clinical cloze-style reading comprehension. Int. J. Environ. Res. Public Health 17(4), 1323 (2020)
Rafi A.M., et al.: Application of DenseNet in camera model identification and post-processing detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 19–28 (2019)
Chen, X., Du, J., Zhang, H.: Lipreading with DenseNet and resBi-LSTM. SIViP 14(5), 981–989 (2020). https://doi.org/10.1007/s11760-019-01630-1
Chen, Y., Christodoulou, A.G., Zhou, Z., Shi, F., Xie, Y., Li, D.: MRI super-resolution with GAN and 3D multi-level DenseNet: smaller, faster, and better. arXiv preprint arXiv:2003.01217, 2 March 2020
Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D., Xiao, P.: Skin capacitive imaging analysis using deep learning GoogLeNet. In: Advances in Intelligent Systems and Computing, 16 July 2020
Balagourouchetty, L., Pragatheeswaran, J.K., Pottakkat, B., Ramkumar, G.: GoogLeNet based ensemble FCNet classifier for focal liver lesion diagnosis. IEEE J. Biomed. Health Inform. 24(6), 1686–1694 (2020). https://ieeexplore.ieee.org/abstract/document/8845663
Kim, J.H., Seo, S.Y., Song, C.G., Kim, K.S.: Assessment of electrocardiogram rhythms by GoogLeNet deep neural network architecture. J. Healthc. Eng. 2019, 1–10 (2019). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512052/
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 1(42), 60–88 (2017)
Xu, Y., et al.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1626–1630. IEEE, 4 May 2014
López-Linares Román, K., García Ocaña, M.I., Lete Urzelai, N., González Ballester, M.Á., Macía Oliver, I.: Medical image segmentation using deep learning. In: Chen, Y.-W., Jain, L.C. (eds.) Deep Learning in Healthcare. ISRL, vol. 171, pp. 17–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32606-7_2
Tafti, A.P., Bashiri, F.S., LaRose, E., Peissig, P.: Diagnostic classification of lung CT images using deep 3D multi-scale convolutional neural network. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 412–414. IEEE, 4 June 2018
Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:2003.13815, 26 March 2020
Acknowledgment
This work was supported by the National Institutes of Health (NIH) grants R01AR73147 and P30AR76312.
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Yan, S. et al. (2020). DeepTKAClassifier: Brand Classification of Total Knee Arthroplasty Implants Using Explainable Deep Convolutional Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_12
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DOI: https://doi.org/10.1007/978-3-030-64559-5_12
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