A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty.

Abstract Background Inappropriate acetabular component angular position is believed to increase the risk of hip dislocation following total hip arthroplasty (THA). However, manual measurement of these angles is time consuming and prone to inter-observer variability. The purpose of this study was to develop a deep learning tool to automate the measurement of acetabular component angles on postoperative radiographs. Methods Two cohorts of 600 anteroposterior (AP) pelvis and 600 cross-table lateral hip postoperative radiographs were used to develop deep learning models to segment the acetabular component and the ischial tuberosities. Cohorts were manually annotated, augmented, and randomly split to train-validation-test datasets on an 8:1:1 basis. Two U-Net convolutional neural network (CNN) models (one for AP and one for cross-table lateral radiographs) were trained for 50 epochs. Image processing was then deployed to measure the acetabular component angles on the predicted masks on anatomical landmarks. Performance of the tool was tested on 80 AP and 80 cross-table lateral radiographs. Results The CNN models achieved a mean Dice Similarity Coefficient of 0.878 and 0.903 on AP and cross-table lateral test datasets, respectively. The mean difference between human-level and machine-level measurements was 1.35° (σ=1.07°) and 1.39° (σ=1.27°) for the inclination and anteversion angles, respectively. Differences of 5⁰ or more between human-level and machine-level measurements were observed in less than 2.5% of cases. Conclusions We developed a highly accurate deep learning tool to automate the measurement of angular position of acetabular components for use in both clinical and research settings. Level of Evidence III