Automatic Evaluation of Bone Age Using Hand Radiographs and Pancorporal Radiographs in Adolescent Idiopathic Scoliosis

Background/Objectives: Adolescent idiopathic scoliosis (AIS) is a complex, three-dimensional spinal deformity that requires monitoring of skeletal maturity for effective management. Accurate bone age assessment is important for evaluating developmental progress in AIS. Traditional methods rely on ossification center observations, but recent advances in deep learning (DL) might pave the way for automatic grading of bone age. Methods: The goal of this research is to propose a new deep neural network (DNN) and evaluate class activation maps for bone age assessment in AIS using hand radiographs. We developed a custom neural network based on DenseNet201 and trained it on the RSNA Bone Age dataset. Results: The model achieves an average mean absolute error (MAE) of 4.87 months on more than 250 clinical testing AIS patient dataset. To enhance transparency and trust, we introduced Score-CAM, an explainability tool that reveals the regions of interest contributing to accurate bone age predictions. We compared our model with the BoneXpert system, demonstrating similar performance, which signifies the potential of our approach to reduce inter-rater variability and expedite clinical decision-making. Conclusions: This study outlines the role of deep learning in improving the precision and efficiency of bone age assessment, particularly for AIS patients. Future work involves the detection of other regions of interest and the integration of other ossification centers.