Automated Risser Grade Assessment of Pelvic Bones Using Deep Learning

(1) Background: This study aimed to develop a deep learning model using a convolutional neural network (CNN) to automate Risser grade assessment from pelvic radiographs. (2) Methods: We used 1619 pelvic radiographs from patients aged 12-18 years with scoliosis to train two CNN models-one for the right pelvis and one for the left. A multimodal approach incorporated 224 × 224-pixel regions of interest from radiographs, alongside patient age and gender. The models were optimized with Adam, weight decay, rectified linear unit (ReLU) activation, dropout, and batch normalization, while synthetic data augmentation addressed class imbalance. Performance was evaluated through accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC AUC). (3) Results: The right pelvis model achieved 83.64% accuracy; the left pelvis model reached 80.56%. Both models performed well for Risser Grades 0, 2, and 4, with the right pelvis model achieving a microaverage F1-score of 0.836 and ROC AUC of 0.895. The left pelvis model achieved a microaverage F1-score of 0.806 and ROC AUC of 0.872. Challenges arose from class imbalance in less frequent grades. (4) Conclusions: CNN models effectively automated Risser grade assessment, reducing clinician workload and variability.