Artificial intelligence for pediatric height prediction using large-scale longitudinal body composition data.

Objective: We developed a precise, reliable artificial intelligence (AI) model for predicting the future height of children and adolescents based on anthropometric and body composition data. Materials and Methods: We used an extensive longitudinal dataset from a large-scale Korean cohort study, which included 588,546 measurements from 96,485 children and adolescents aged 7-18. We developed a prediction model using the light gradient boosting method and integrated anthropometric and body composition metrics along with their standard deviation scores (SDSs) and velocity parameters. Model performance was assessed through root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). We employed Shapley additive explanations (SHAP) for model interpretability. Results: The model accurately predicted future heights. For males, the average RMSE, MAE, and MAPE were 2.51 cm, 1.74 cm, and 1.14%, respectively, with female prediction results showing comparable accuracy (2.28 cm, 1.68 cm, and 1.13%, respectively). Shapley additive explanations analysis revealed that the SDS of height, height velocity, and soft lean mass velocity were key predictors of future height. The model created personalized growth curves through estimation of individual-specific height trajectories, comparison with actual measurements, and identification of key variables using local SHAP values. Conclusion: Our model produces accurate and personalized growth curves, incorporating explainable AI techniques for enhanced clinical understanding. This method advances pediatric growth assessment and provides robust clinical decision support. Despite limitations including the absence of handwrist radiography comparison and Korean population specificity, our approach demonstrates significant potential for early identification of growth disorders and optimization of growth outcomes.