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- Title
Performance comparison of visual transformer based models for shoulder implant classification.
- Authors
Kablan, Elif Baykal; Kablan, Yavuz
- 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. 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.
- Subjects
ELECTRIC transformers; ARTHROPLASTY; X-ray imaging; PROSTHETICS; SHOULDER joint
- Publication
Nigde Omer Halisdemir University Journal of Engineering Sciences / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 2024, Vol 13, Issue 2, p704
- ISSN
2564-6605
- Publication type
Article
- DOI
10.28948/ngumuh.1400666