Deep Learning and Computer Vision Techniques for Automated Total Hip Arthroplasty Planning on 2-D Radiographs

Abstract

Preoperative planning is mandatory for successful total hip arthroplasty (THA). In planning, the operating surgeon should decide the best type and size of THA components for the patient. However, most digital templating software only simulates acetate templating by overlaying the shape of the prosthesis components on a radiograph; the selection and positioning of the prostheses are performed manually depending on the operator’s experience. Determining the optimal type and size of THA components is a repetitive and time-consuming task for digital and acetate templating. This study proposes a novel approach to automatically select and position THA components that are most suitable for the patient’s bone anatomy. The approach consists of two phases: segmenting a hip anteroposterior (AP) radiographic image into five predefined anatomical regions using a fully convolutional neural network, and estimating the optimal sizes and positions of THA components using deep learning and computer vision technology. The experiments demonstrated that the accuracy of acetabular and femoral component size prediction within one size error was 78.9% and 70.9%, respectively. Compared with meta-analysis results from previous studies, our results are close to human level. An automated digital templating prototype system was developed using our research results and tested in a clinical setting to evaluate field adaptability. These processes are introduced in this study.