Predictive Methods for Estimating Growth in Children: Clinical and Sports Perspectives
DOI:
https://doi.org/10.12775/QS.2026.58.72665Keywords
growth estimation, growth in children, pediatric endocrinology, skeletal age, mid-parental height, machine learning, longitudinal cohort studies, youth athletes, biological maturation, growth velocity in children, Tanner stagesAbstract
Background: Accurate growth estimation in children is crucial in pediatric medicine and youth sports. It enables early detection of hormonal or systemic disorders and supports talent identification, structured training, and injury prevention. Traditional methods include mid-parental height calculation and skeletal age assessment using radiographs of the left hand and wrist via Greulich–Pyle (GP) atlas or Tanner–Whitehouse (TW2/TW3) scoring. Accuracy may decline during rapid growth phases, especially puberty.
Aim: This review synthesizes evidence on growth prediction methods, comparing conventional techniques with emerging data-driven and automated approaches, highlighting clinical and sports applications.
Materials and Methods: Literature analysis focused on conventional radiography, ultrasound (BonAge®), MRI, and computer-assisted systems (CASAS, CASMAS, BoneXpert). Evaluations considered observer variability, efficiency, suitability for serial monitoring, and correlation with chronological age.
Results: GP atlas allows rapid assessment but has higher inter-observer variability. TW scoring improves reproducibility for longitudinal tracking. Ultrasound is non-invasive, radiation-free, and correlates well with radiographs but may misestimate advanced or delayed bone age. MRI provides detailed growth plate visualization with high reliability but is costlier and time-consuming. Automated systems reduce observer bias: CASAS enables semi-automated multi-bone scoring, CASMAS focuses on single phalanx automation, and BoneXpert performs fully automated multi-bone analysis with high accuracy. Mid-parental height remains a valuable adjunct for contextualizing skeletal age.
Conclusions: Skeletal age assessment has evolved toward safer, reproducible, and automated approaches. Combining conventional and advanced methods with mid-parental height allows comprehensive growth evaluation, optimizing monitoring and decision-making in healthcare and sports.
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