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  1. Home
  3. Artificial Intelligence For Pediatric Height Prediction Using Large-scale Longitudinal Body Composition Data.
  1. Home
  3. Artificial Intelligence For Pediatric Height Prediction Using Large-scale Longitudinal Body Composition Data.

Related Experiment Video

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

Dohyun Chun1,2, Hae Woon Jung3, Jongho Kang2,4

  • 1College of Business Administration, Kangwon National University, Chuncheon, Korea.

Digital Health
|November 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We created an AI model to predict children's future height using anthropometric data. This tool offers accurate, personalized growth curves, aiding in early detection of growth disorders.

Keywords:
Height predictionbody compositionexplainable artificial intelligencegrowth velocitiespersonalized growth curves

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Area of Science:

  • Pediatric endocrinology and growth assessment.
  • Artificial intelligence in healthcare.
  • Biometric data analysis for human development.

Background:

  • Accurate prediction of childhood and adolescent height is crucial for monitoring growth and identifying potential disorders.
  • Traditional growth assessment methods may lack precision and personalization.
  • Advancements in AI offer new possibilities for sophisticated predictive modeling in pediatrics.

Purpose of the Study:

  • To develop and validate a precise artificial intelligence (AI) model for predicting future height in children and adolescents.
  • To leverage anthropometric and body composition data for accurate growth trajectory estimation.
  • To enhance clinical decision support in pediatric growth assessment.

Main Methods:

  • Utilized a large-scale Korean longitudinal cohort dataset (96,485 children, 588,546 measurements).
  • Developed a prediction model using the light gradient boosting method, incorporating anthropometric metrics, body composition, SDSs, and velocity parameters.
  • Assessed model performance using RMSE, MAE, and MAPE; employed SHAP for interpretability.

    • Main Results:

    • The AI model demonstrated high accuracy in predicting future heights for both males and females (RMSE < 2.51 cm).
    • Key predictors identified include height SDS, height velocity, and soft lean mass velocity.
    • Generated personalized growth curves by estimating individual height trajectories and identifying critical variables.

    Conclusions:

    • The developed AI model provides accurate, personalized growth curves with explainable AI insights.
    • This approach advances pediatric growth assessment and supports clinical decision-making for growth disorders.
    • The model shows significant potential for early identification and management of growth abnormalities.