Artificial Intelligence-Based Surgery Support Model Using Intraoperative Radiographs for Assessing the Acetabular Component Angle.

Abstract

This study aimed to develop an artificial intelligence-based surgical support model for assessing the acetabular component angle using intraoperative radiographs during total hip arthroplasty and verify its accuracy. A total of 268 hips were analyzed. At first, 268 preoperative and intraoperative anteroposterior pelvic radiographs were amplified to 536. These radiographs were used to create a learning model to estimate the acetabular component angle from the radiographs intraoperatively. The ground truth was the anteversion and inclination angles obtained from the computed tomography-based navigation system intraoperatively. Bone landmarks on the preoperative and intraoperative radiographs were manually annotated. The distances and angles between each landmark were used as predictor variables. The estimation accuracy was assessed for internal and external test datasets. Mean absolute error (MAE) and R² values were used as accuracy measures. The MAE and R² for the internal test set showed 2.19 and 0.850 for anteversion, and 1.18 and 0.805 for inclination, respectively. The MAE and R² for the external test set showed 2.78 and 0.789 for anteversion, and 1.56 and 0.744 for inclination, respectively. We developed an artificial intelligence-based surgical support model for accurately assessing the acetabular component angle using intraoperative radiographs. Excellent estimation accuracy was confirmed for the external test set. In the future, the model may help to reduce the risk of adverse postoperative events.