Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

• DOI: 10.1002/jor.24617
• Publication Date: 2020-01-30T09:17:46Z
• Authors: Alireza Borjali; Antonia F. Chen; Orhun K. Muratoglu; Mohammad A. Morid; Kartik M. Varadar...
• Keywords: Aged, 80 and over; Male; FOS: Computer and information sciences; Arthroplasty, Replacement, Hip; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; Electrical Engineering and Systems Science - Image and Video Processing; Middle Aged; Prosthesis Design; Radiography; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; FOS: Electrical engineering, electronic engineering, information engineering; Humans; Female; Hip Joint; Hip Prosthesis; Aged; Retrospective Studies

AbstractIdentifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time‐consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.