Programa del congreso

Hip dysplasia is an orthopedic condition associated with an atypically shaped acetabulum, leading to hip instability. The primary tool for assessing acetabular coverage is the x-ray once ossification of the femoral head has commenced. Key indicators such as Tönnis and Wiberg angles can be measured, which will be the focus of this study. These angles are currently measured manually on radiographs, which introduces variability, motivating the development of automated methods. Therefore, we propose BAGGY©, a deep learning-based software for automatic detection of anatomical landmarks and measurement of Wiberg and Tönnis angles. The system employs a Faster Region-based Convolutional Neural Network (Faster R-CNN) with a Residual Network-50 (ResNet-50) and a Feature Pyramid Network (FPN) backbone, pretrained on Common Objects in Context (COCO), fine-tuned with custom detection heads for landmark detection. We collected 101 radiographs for training, validation and testing. A 5-fold cross-validation method was employed in which landmark detection achieved a mean average precision of 0.76 for both angles. On the independent test set annotated by a doctor, deviations in landmark predictions averaged ~ 5 pixels. Angle measurements showed strong agreement with the doctor (Pearson’s correlation coefficient r~0.85, intraclass correlation coefficient ICC>0.8), with the Tönnis angle demonstrating higher precision (2.9º) than the Wiberg angle (5.0º). Automatic angle computation was feasible in most radiographs (76.9% for Wiberg, and 84.6% for Tönnis). These results support the potential applicability of the proposed approach to assist in the evaluation of hip dysplasia