Akademik Veri Yönetim Sistemi
Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, cilt.13, sa.2, ss.704-712, 2024 (Hakemli Dergi)
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. Therefore, there is a need for highly accurate automated diagnostic systems to help recognize unknown implants. 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 of pre-trained ViT models on a publicly available shoulder X-ray dataset showed high success in terms of accuracy, precision, sensitivity, and F-measure metrics. The Swin-B model yielded the highest results with 93.84% accuracy, 88.15% precision, and 85.52% recall. These results showed that ViT based models can help improve treatment planning by providing reliable identification of shoulder implant manufacturers and model information and time efficiency, especially for specialists.