Classifying shoulder implants in X-ray images using deep learning

Computational and Structural Biotechnology Journal

卷:18

Maya Stark¹  Kazunori Okada¹  Pierre Baldi²  Saman Porhemmat²  Gregor Urban²  Brian Feeley³ 

[1] San Francisco State University, Computer Science Department, San Francisco, CA, USA;

[2] University of California, Irvine School of Information and Computer Sciences, Irvine, CA, USA;

[3] University of California, San Francisco, Department of Orthopaedic Surgery, San Francisco, CA, USA;

关键词: Deep learning;    Computer vision;    Orthopedics;    X-ray imaging;    Total shoulder arthroplasty;   

DOI  :  

来源: DOAJ

【 摘 要 】

Total Shoulder Arthroplasty (TSA) is a type of surgery in which the damaged ball of the shoulder is replaced with a prosthesis. Many years later, this prosthesis may be in need of servicing or replacement. In some situations, such as when the patient has changed his country of residence, the model and the manufacturer of the prosthesis may be unknown to the patient and primary doctor. Correct identification of the implant’s model prior to surgery is required for selecting the correct equipment and procedure. We present a novel way to automatically classify shoulder implants in X-ray images. We employ deep learning models and compare their performance to alternative classifiers, such as random forests and gradient boosting. We find that deep convolutional neural networks outperform other classifiers significantly if and only if out-of-domain data such as ImageNet is used to pre-train the models. In a data set containing X-ray images of shoulder implants from 4 manufacturers and 16 different models, deep learning is able to identify the correct manufacturer with an accuracy of approximately 80% in 10-fold cross validation, while other classifiers achieve an accuracy of 56% or less. We believe that this approach will be a useful tool in clinical practice, and is likely applicable to other kinds of prostheses.

【 授权许可】

Unknown