Automated detection of shoulder arthroplasty in X-Rays using machine learning

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Brunt, Andrew and Lawley, Alistair and Clarke, Jon and Walmsley, Philip and Riches, Phil and Dobie, Gordon; (2025) Automated detection of shoulder arthroplasty in X-Rays using machine learning. In: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . IEEE, DNK. ISBN 979-8-3315-8618-8 (https://doi.org/10.1109/EMBC58623.2025.11252672)

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Abstract

Demand for shoulder arthroplasty is rising at a faster rate than hip and knee arthroplasty, driven by an increasingly aging yet active population. Joint registries are playing an increasingly critical role in tracking the long-term success of shoulder arthroplasty, identifying failure mechanisms, and shaping clinical best practices but current classification procedures are often performed by non-medically trained encoders leading to error. This study examines the use of machine learning in techniques to classify four broad categories of shoulder arthroplasty technique from postoperative x-rays. Data from the Scottish Arthroplasty Project, was used to create a balanced dataset of 1000 samples. A 10-fold cross validation was used for the training of 4 neural network models commonly used for classification of x-ray data. InceptionV3 model achieved the highest overall performance with an accuracy of 93.85% after cross validation, while EfficientNet demonstrated the highest individual classifier accuracy of 99% suggesting the potential to increase accuracy further in future studies. Clinical Relevance - This research highlights the potential of machine learning to enhance the accuracy of joint registry data encoding, leading to more precise insights into implant survival and revision rates. By improving data reliability, machine learning can drive evidence-based advancements in implant design and surgical techniques, ultimately ensuring better patient outcomes.

ORCID iDs

Brunt, Andrew, Lawley, Alistair ORCID logoORCID: https://orcid.org/0000-0002-0903-1116, Clarke, Jon, Walmsley, Philip, Riches, Phil ORCID logoORCID: https://orcid.org/0000-0002-7708-4607 and Dobie, Gordon ORCID logoORCID: https://orcid.org/0000-0003-3972-5917;
CORE (COnnecting REpositories)