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Related Experiment Video

Updated: Jan 9, 2026

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
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Automated Detection of Shoulder Arthroplasty in X-rays Using Machine Learning.

Andrew Brunt, Alistair Lawley, Philip Riches

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    Machine learning accurately classifies shoulder arthroplasty techniques from X-rays, improving joint registry data. This enhances implant design and surgical approaches for better patient outcomes.

    Area of Science:

    • Orthopedic surgery
    • Medical imaging
    • Machine learning

    Background:

    • Rising demand for shoulder arthroplasty due to an aging, active population.
    • Joint registries are crucial for tracking long-term outcomes but face data encoding errors.
    • Current classification methods rely on non-medical encoders, introducing inaccuracies.

    Purpose of the Study:

    • To investigate machine learning (ML) for classifying shoulder arthroplasty techniques from postoperative X-rays.
    • To assess the performance of neural network models in this classification task.
    • To improve the accuracy of data within joint registries for shoulder arthroplasty.

    Main Methods:

    • Utilized a balanced dataset of 1000 samples from the Scottish Arthroplasty Project.
    • Trained four common neural network models using 10-fold cross-validation.

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    Last Updated: Jan 9, 2026

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  • Classified four broad categories of shoulder arthroplasty techniques from X-ray images.

    • Main Results:

    • The InceptionV3 model achieved the highest overall accuracy (93.85%) after cross-validation.
    • The EfficientNet model demonstrated the highest individual classifier accuracy (99%).
    • These results indicate significant potential for ML in improving data accuracy.

    Conclusions:

    • Machine learning shows strong potential to enhance the accuracy of joint registry data encoding for shoulder arthroplasty.
    • More accurate data can facilitate evidence-based improvements in implant design and surgical techniques.
    • Improved data accuracy will lead to better insights into implant survival and revision rates.