Detection and classification of shoulder implants from X-ray images: YOLO and pretrained convolution neural network based approach

Abstract

Shoulder implants may need to be replaced several months or years after insertion. In this case, it is important to determine the manufacturer or model of the implant. In some cases, the implant manufacturer and model may not be known to patients or their physicians due to uncertainty in medical records. Today, the task of identifying an implant manufacturer or model in such situations relies on meticulous examination and visual comparison of X-ray images taken from the implant by medical professionals. But this identification task is often time-consuming, error-prone and difficult for both radiologists and orthopedic surgeons. In this study, it is aimed to automatically detect the implant manufacturer using deep learning methods. For this purpose, pretrained CNN architectures and cascade models consisting of feeding these architectures with the YOLO algorithm have been proposed.