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Demystifying Deep Learning Techniques in Knee Implant Identification

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Emerging Trends and Applications in Artificial Intelligence ( ICETAI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 960))

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Abstract

Accurate identification of an orthopedic implant before a revision surgery is very important and helps both physicians and patients in numerous aspects. The proposed system uses a novel framework to identify five different total knee arthroplasty implants from plain X-ray images using Deep learning techniques. Anterior-Posterior and Lateral images are used together in this study to make identification much more accurate. The proposed system identifies five different knee implants with an accuracy of 86.25% and an Area Under curve of 0.974.

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References

  1. Feng, J.E., Novikov, D., Anoushiravani, A.A., Schwarzkopf, R.: Total knee arthroplasty: improving outcomes with a multidisciplinary approach. J. Multidisciplinary Healthcare, 63–73 (2018)

    Google Scholar 

  2. Ditton, E., et al.: Improving patient outcomes following total knee arthroplasty: identifying rehabilitation pathways based on modifiable psychological risk and resilience factors. Front. Psychol. 11, 1061 (2020)

    Article  Google Scholar 

  3. Malkani, A.L., et al.: The difficult primary total knee arthroplasty. Instr. Course Lect. 65, 243–265 (2016)

    Google Scholar 

  4. Lee, D.W., et al.: Automated detection of surgical implants on plain knee radiographs using a deep learning algorithm. Medicina 58(11), 1677 (2022)

    Article  Google Scholar 

  5. Gao, J., Xing, D., Dong, S., Lin, J.: The primary total knee arthroplasty: a global analysis. J. Orthop. Surg. Res. 15, 1–12 (2020)

    Article  Google Scholar 

  6. Varacallo, M., Luo, T.D., Johanson, N.A.: Total knee arthroplasty techniques (2018)

    Google Scholar 

  7. Kim, T.W., Kang, S.B., Chang, C.B., Moon, S.Y., Lee, Y.K., Koo, K.H.: Current trends and projected burden of primary and revision total knee arthroplasty in Korea between 2010 and 2030. J. Arthroplasty 36(1), 93–101 (2021)

    Article  Google Scholar 

  8. Dy, C.J., Bozic, K.J., Padgett, D.E., Pan, T.J., Marx, R.G., Lyman, S.: Is changing hospitals for revision total joint arthroplasty associated with more complications? Clin. Orthop. Relat. Res. 472, 2006–2015 (2014)

    Google Scholar 

  9. Lee, D.H., Lee, S.H., Song, E.K., Seon, J.K., Lim, H.A., Yang, H.Y.: Causes and clinical outcomes of revision total knee arthroplasty (2017)

    Google Scholar 

  10. Postler, A., Lützner, C., Beyer, F., Tille, E., Lützner, J.: Analysis of total knee arthroplasty revision causes. BMC Musculoskelet. Disord. 19, 1–6 (2018)

    Article  Google Scholar 

  11. Borjali, A., Chen, A.F., Muratoglu, O.K., Morid, M.A., Varadarajan, K.M.: Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J. Orthop. Res. 38(7), 1465–1471 (2020)

    Google Scholar 

  12. Goceri, E.: Medical image data augmentation: techniques, comparisons, and interpretations. Artif. Intell. Rev. 56, 12561–12605 (2023)

    Google Scholar 

  13. Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., Azim, M.A.: Transfer learning: a friendly introduction. J. Big Data 9(1), 102 (2022)

    Article  Google Scholar 

  14. Sharma, S., et al.: Knee Implant Identification by Fine-Tuning Deep Learning Models (2021)

    Google Scholar 

  15. Tiwari, A., Yadav, A.K., Bagaria, V.: Application of deep learning algorithm in the automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: are algorithms better than humans? (2022)

    Google Scholar 

  16. Ghose, S., Datta, S., Batta, V., Malathy, C., Gayathri, M.: Artificial intelligence-based identification of total knee arthroplasty implants. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 302–307. IEEE (2020)

    Google Scholar 

  17. Belete, S.C., Batta, V., Kunz, H.: Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network. Inform. Med. Unlocked 25, 100669 (2021)

    Article  Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  19. Sudha, V., Ganeshbabu, T.R.: A convolutional neural network classifier VGG-19 architecture for lesion detection and grading in diabetic retinopathy based on deep learning. CMC-Comput. Mater. Continua 66(1), 827–842 (2021)

    Article  Google Scholar 

  20. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. Yu, T., Zhu, H.: Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)

  23. Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)

  24. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  25. Lydia, A., Francis, S.: Adagrad—an optimizer for stochastic gradient descent. Int. J. Inf. Comput. Sci. 6(5), 566–568 (2019)

    Google Scholar 

  26. Vakili, M., Ghamsari, M., Rezaei, M.: Performance analysis and comparison of machine and deep learning algorithms for IoT data classification. arXiv preprint arXiv:2001.09636 (2020)

  27. Ramanathan, A., Christy Bobby, T.: Classification of corpus callosum layer in mid-saggital MRI images using machine learning techniques for autism disorder. In: Saha, S., Nagaraj, N., Tripathi, S. (eds.) MMLA 2019. CCIS, vol. 1290, pp. 78–91. Springer, Singapore (2020). https://doi.org/10.1007/978-981-33-6463-9_7

    Chapter  Google Scholar 

  28. Xiao, M., et al.: Addressing the overfitting problem in deep learning-based solutions for next-generation data-driven networks. Wirel. Commun. Mob. Comput. 2021, 1–10 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, SRM Institute of Science and Technology, Chennai, India

    Shaswat Srivastava, A. Ramanathan, C. Malathy & M. Gayathri

  2. Department of Orthopaedics, Apollo Speciality Hospital, OMR, Chennai, Tamil Nadu, India

    Puthur R. Damodaran

  3. Department of Orthopaedics and Trauma, Luton, Dunstable University Hospital NHS Trust, Luton, UK

    Vineet Batta

Authors
  1. Shaswat Srivastava
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  2. A. Ramanathan
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  3. Puthur R. Damodaran
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  4. C. Malathy
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  5. M. Gayathri
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  6. Vineet Batta
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Corresponding author

Correspondence to Vineet Batta .

Editor information

Editors and Affiliations

  1. Ingenium Research Group, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Akhtar Jamil

  3. Department of Computer Engineering, Istinye University, Istanbul, Türkiye

    Alaa Ali Hameed

  4. Ingenium Research Group, University of Castilla-La Mancha (UCLM), Ciudad Real, Spain

    Isaac Segovia Ramírez

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Srivastava, S., Ramanathan, A., Damodaran, P.R., Malathy, C., Gayathri, M., Batta, V. (2024). Demystifying Deep Learning Techniques in Knee Implant Identification. In: García Márquez, F.P., Jamil, A., Hameed, A.A., Segovia Ramírez, I. (eds) Emerging Trends and Applications in Artificial Intelligence. ICETAI 2023. Lecture Notes in Networks and Systems, vol 960. Springer, Cham. https://doi.org/10.1007/978-3-031-56728-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-56728-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56727-8

  • Online ISBN: 978-3-031-56728-5

  • eBook Packages: EngineeringEngineering (R0)

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