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Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study

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Medical Image Understanding and Analysis (MIUA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14122))

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Abstract

Orthopedic implant identification is an important and necessary step prior to performing revision surgery of different joints. The inability to identify an implant can lead to significant surgical difficulties with consequent unfavorable outcomes. This paper proposes a novel framework to identify the make and model of seven (7) different total shoulder arthroplasty implants utilizing plain X-ray images and Artificial intelligence. The proposed work classified implants with an accuracy of 91.48% and with an AUC (Area under curve) of 0.9932 showing higher effectiveness in orthopedic implant identification. Further work is required to enhance and progress this work, with a goal of greater accuracy and fewer errors.

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Authors and Affiliations

  1. Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India

    Aakriti Mishra, M. Gayathri & D. Vathana

  2. Department of Orthopedics and Trauma, Luton and Dunstable University Hospital NHS Trust, Luton, UK

    Vineet Batta

  3. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Soumya Snigdha Kundu

  4. Department of Orthopaedic Surgery and Sports Medicine, Elbow Shoulder Research Center, University of Kentucky, UK HealthCare, Lexington, KY, USA

    Srinath Kamineni

  5. Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India

    A. Ramanathan & C. Malathy

Authors
  1. Aakriti Mishra
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  2. A. Ramanathan
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  3. Vineet Batta
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  4. C. Malathy
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  5. Soumya Snigdha Kundu
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  6. M. Gayathri
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  7. D. Vathana
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  8. Srinath Kamineni
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Corresponding author

Correspondence to Vineet Batta .

Editor information

Editors and Affiliations

  1. University of Aberdeen, Aberdeen, UK

    Gordon Waiter

  2. University of Aberdeen, Aberdeen, UK

    Tryphon Lambrou

  3. University of Aberdeen, Aberdeen, UK

    Georgios Leontidis

  4. University of Aberdeen, Aberdeen, UK

    Nir Oren

  5. University of Aberdeen, Aberdeen, UK

    Teresa Morris

  6. University of Aberdeen, Aberdeen, UK

    Sharon Gordon

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Mishra, A. et al. (2024). Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study. In: Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S. (eds) Medical Image Understanding and Analysis. MIUA 2023. Lecture Notes in Computer Science, vol 14122. Springer, Cham. https://doi.org/10.1007/978-3-031-48593-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-48593-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48592-3

  • Online ISBN: 978-3-031-48593-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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