Shoulder Implant Manufacturer Detection by Using Deep Learning: Proposed Channel Selection Layer

Abstract

Total Shoulder Arthroplasty (TSA) is the process of replacing the damaged ball and socket joint in the shoulder with a prosthesis made with polyethylene and metal components. After this procedure, intervention may be required as a result of damage to the prosthesis, except for the need for an examination regarding the prosthesis at certain periods. If the patient does not have information about the model and manufacturer of the prosthesis, the treatment process is delayed. Artificial intelligence-assisted systems can speed up the treatment process by classifying the manufacturer and model of the prosthesis. In this study, artificial intelligence methods were applied to classify shoulder implants using X-ray images. The model and manufacturer of the prosthesis is detected by using the proposed deep learning method. Besides, the most commonly used machine learning classifiers were applied for the same problem to compare the results and show the effectiveness of the proposed method. In addition, the accuracy and precision analysis and measurements of the processing times for the applied methods were performed to reveal the performance, accuracy, and efficiency of the study. In order to measure the performance of the proposed method, it was compared with studies on the same problem in the literature. As a result of the comparison, it was found that the proposed method, with an accuracy rate of 97.2%, performed better than the other studies. In the study, the implant manufacturer and model are classified in order to carry out the implant surgery process in the best way with the proposed deep learning model. With the success of the proposed system, the applicability of this model in similar prosthesis classifications has been shown. Differently from the studies in the literature, the channel selection formula is presented in the proposed deep learning method recommended for selecting the most distinctive feature filters.