Omuz implantı sınıflandırmasında görü dönüştürücü tabanlı modellerin performans karşılaştırması

Year 2024, , 704 - 712, 15.04.2024

Elif Baykal Kablan , Yavuz Kablan

https://doi.org/10.28948/ngumuh.1400666

Abstract

Total omuz artroplastisi (TSA), şiddetli ağrı ve kısıtlı omuz eklemi hareketini ele alan cerrahi bir prosedürdür. TSA ameliyatı sırasında röntgen görüntüleri, farklı üreticiler tarafından üretilen çeşitli modeller arasından hastaya uygun protez implantın seçimine rehberlik etmektedir. Bununla birlikte, protezler zamanla aşınabilir veya gevşeyebilir, bu nedenle periyodik değerlendirme ve değiştirme gerektirmektedir. Halihazırda bu süreç, hastalardan yeni röntgen görüntülerinin alınmasını gerektirmekte ve implant tiplerine ilişkin uzman görüşlerinde değişkenliğe neden olmaktadır. Bu nedenle, bilinmeyen implantları tanımaya yardımcı olacak yüksek doğrulukta otomatik teşhis sistemlerine ihtiyaç vardır. Bu çalışmada, X-ray görüntülerinden otomatik omuz implantı sınıflandırması için görü dönüştürücü (ViT) tabanlı modellerin performans karşılaştırması sunulmaktadır. Önceden eğitilmiş ViT modellerinin herkese açık bir veri kümesi üzerinde ince ayarı doğruluk, hassasiyet, duyarlılık ve F-ölçümü metriklerinde yüksek başarı göstermiştir. Swin-B modeli %93.84 doğruluk, %88.15 kesinlik ve %85.52 duyarlılık ile en yüksek sonuçları vermiştir. Bu sonuçlar, ViT tabanlı modellerin omuz implantı üreticilerinin ve model bilgilerinin güvenilir bir şekilde tanımlanmasını ve özellikle uzmanlar için zaman verimliliği sağlayarak tedavi planlamasının iyileştirilmesine yardımcı olabileceğini göstermiştir.

Keywords

Total omuz artroplastisi, Omuz implantları, X-ray, Sınıflandırma, Görü dönüştürücü

Performance comparison of visual transformer based models for shoulder implant classification

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.

Keywords

Total shoulder arthroplasty, Shoulder implants, X-ray, Classification, Vision transformer

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