Potential of Artificial Intelligence for Bone Age Assessment in Iranian Children and Adolescents: An Exploratory Study

Our findings demonstrate that the proposed deep learning model achieves reasonable accuracy in BA assessment, with stronger performance in girls compared to boys. However, the relatively wide 95% LoA, particularly for boys, and prediction errors at the extremes of the age range highlight the need for further refinement and validation. While the model shows potential as a supplementary tool for clinicians, future studies should focus on improving prediction accuracy, reducing variability, and validating the model on larger, more diverse datasets before considering widespread clinical implementation. Additionally, addressing edge cases and specific conditions that a human reviewer may detect but the model might overlook, will be essential for enhancing its clinical reliability.