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Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Updated: Jul 12, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

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Published on: November 4, 2025

Integrated framework for pediatric height assessment: X-ray-based height extreme cases classification and machine

Ya-Wen Chang¹, Meng-Che Tsai², Sun-Yuan Hsieh^3,4,5,6,7

¹Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701, Taiwan ROC.

Scientific Reports

|July 9, 2026

Summary

This study introduces a new AI framework to predict children's future height abnormalities using hand X-rays and clinical data. The system accurately identifies extreme height cases and forecasts growth trajectories for better pediatric care.

Keywords:

Artificial Intelligence Bone Age Deep Learning Growth Abnormalities Hand X-ray Height Prediction Machine Learning

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Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

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Last Updated: Jul 12, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

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Published on: November 4, 2025

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

Published on: June 7, 2024

Area of Science:

Pediatric endocrinology and growth assessment.
Artificial intelligence in medical diagnostics.
Biometric data analysis for human growth.

Background:

Accurate pediatric height assessment is vital for monitoring growth and timely interventions.
Traditional methods often focus on current bone age, limiting prediction of future height deviations.
There is a need for advanced tools to predict potential extreme height outcomes in children.

Purpose of the Study:

To develop and validate an integrated AI framework for predicting pediatric height abnormalities.
To identify children at risk for extreme short or tall stature.
To forecast height trajectories across various time scales for improved clinical decision-making.

Main Methods:

A two-component framework utilizing hand X-ray images and clinical data.
Inception-ResNet-V2 model for classifying potential extreme height cases, with DSEV for data imbalance.
XGBoost model for multivariate height prediction using anthropometric and medical features.
Evaluation of prediction accuracy at 6 months, 1 year, 2 years, and near-final adult height.

Main Results:

Classification models demonstrated high accuracy and reliability in detecting unexpectedly short and tall children.
Multivariate prediction models achieved very low mean absolute error for short-term and long-term height predictions.
The framework showed consistent and stable performance across different prediction horizons.

Conclusions:

The integrated AI framework offers precise and comprehensive tools for pediatric growth evaluation.
This approach enhances the ability to predict and manage future height abnormalities.
It supports clinicians in making informed decisions for growth monitoring and intervention planning.