Comparison of adult height prediction using bone age and body composition for growth assessment in Korean children.

To compare adult height (AH) predictions using body composition-based biological age with those derived from bone age in Korean children. A multicenter, assessor-blinded, prospective study was conducted with 80 healthy children aged 7-13 years. Participants were assessed using two methods: the traditional Tanner-Whitehouse 3 (TW3) bone age method and a model based on artificial intelligence (AI), incorporating body composition metrics such as BMI, fat-free mass, and muscle mass through bioelectrical impedance analysis. The clinical equivalence between the two prediction methods was evaluated, with a non-inferiority margin of 0.661 years. The difference in predicted bone age between the AI-based method and the TW3 method was 0.04 ± 1.02 years, indicating clinical equivalence. Exploratory analysis showed a positive correlation between lean mass and bone age, suggesting that body composition metrics could reflect skeletal maturity. Therefore, the AI-based method utilizing body composition parameters was clinically equivalent to the traditional TW3 method for predicting AH. This approach offers a viable alternative for predicting adult height in pediatric populations, emphasizing the potential for integrating personalized metrics such as body composition into routine growth monitoring; however, further research is needed before it can be widely applied in clinical practice. Future studies should explore its utility in children with growth disorders and refine the model across different growth phases.