Enhancing Pediatric Bone Age Assessment Using Artificial Intelligence: Implications for Orthopedic Surgery

Background Bone age assessment is a critical tool in pediatric orthopedic surgery, guiding treatment decisions for growth-related disorders and surgical interventions. Traditional methods, such as the Greulich-Pyle and Tanner-Whitehouse techniques, rely on manual interpretation of hand and wrist radiographs, making them time-intensive and susceptible to inter-operator variability. Artificial intelligence (AI) has emerged as a promising tool to enhance accuracy, efficiency, and standardization in skeletal maturity assessment. Methods This study evaluates the application of AI in pediatric bone age prediction using the Radiological Society of North America (RSNA) 2017 Pediatric Bone Age Challenge dataset. A deep learning model based on the ResNet-50 architecture (Microsoft Research, Redmond, Washington, USA) was developed and trained on 12,611 hand and wrist radiographs, validated on 1,425 images, and tested on 200 images. Model performance was assessed using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Results The AI model achieved an RMSE of 11.07 months, an MAE of 8.54 months, and an R² of 0.929, indicating strong alignment with radiologist-determined bone ages. The Pearson correlation coefficient (0.963) and Spearman's rank correlation (0.955) confirmed the model's predictive robustness. Compared to traditional methods, which have reported variability with errors ranging from 6 to 18 months, the AI model demonstrated a reduction in inter-operator variability and improved reliability. Conclusion The implementation of AI in bone age assessment offers a more standardized, rapid, and precise alternative to conventional methods. By improving the accuracy and efficiency of skeletal maturity evaluations, AI has significant implications for pediatric orthopedic surgery, optimizing treatment timing and expanding access to high-quality bone age assessments. Further validation studies are needed to ensure clinical applicability across diverse patient populations.