Automated Shoulder Implant Manufacturer Detection using Encoder Decoder based Classifier from X-ray Images.

Abstract

Total shoulder arthroplasty is the process of replacing the damaged ball and socket joint in the shoulder with a prosthesis made with polyethylene and metal components. The prosthesis helps to restore the normal range of motion and reduce pain, enabling the patient to return to their daily activities. These implants may need to be replaced over the years due to damage or wear and tear. It is a tedious and time-consuming process to identify the type of implant if medical records are not properly maintained. Artificial intelligence systems can speed up the treatment process by classifying the manufacturer and model of the prosthesis. We have proposed an encoder-decoder based classifier along with the supervised contrastive loss function that can identify the implant manufacturer effectively with increased accuracy of 92% from X-ray images overcoming the class imbalance problem.