Abstract
Total Shoulder Arthroplasty (TSA) is a surgical procedure addressing severe pain and restricted shoulder joint movement. During TSA surgery, X-ray images guide the selection of the prosthetic implant suitable for the patient from a variety of models produced by different manufacturers. However, prostheses may wear or loosen over time, thus requiring periodic evaluation and replacement. Currently, the process involves taking new X-ray images from patients, resulting in variability in expert opinions on implant types. Automated systems can provide objective assessments, saving time and effort. In this study, we present a performance comparison of Vision Transformer (ViT) based models for automatic shoulder implant classification from X-ray images. Fine-tuning pre-trained ViT models on a shared dataset showed high success in accuracy, precision, sensitivity, and F-measure metrics. This approach can provide reliable identification of shoulder implant manufacturers and model information and time efficiency, especially for specialists, contributing to improving treatment planning.
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