Background
Fractures of the radius and ulna are common injuries in children, with improper healing potentially leading to limitations in forearm rotation affecting function and quality of life. Understanding the normal three-dimensional, age-related morphological variation and spatial relationship of the radius and ulna is essential to support clinical decision-making regarding surgical correction. Previous statistical shape models (SSMs) analyzed the radius and ulna separately, without considering their combined morphological and positional interaction during growth. This study aimed to develop a multi-object SSM of the pediatric forearm to capture combined age-related morphological and spatial variation and to compare its performance with single-object SSMs.

Methods
A cross-sectional dataset of 3D models of healthy pediatric forearms (n = 155; ages 3.8–18.8 years), reconstructed from computed tomography scans, was used to develop a multi-object SSM based on principal component analysis (PCA) and partial least squares regression (PLSR). The model captured the combined shape and position of both bones and enabled prediction of individual forearm geometry across ages during growth. Predictions were validated against follow-up scans of six participants. Morphological accuracy was assessed by root mean squared error (RMSE) and bone length error. Inter-bone spatial relationships were evaluated by comparing normalized distances and bounding box ratios between predicted and original meshes.

Results
The multi-object model captured age-related diaphyseal scaling and epiphyseal development. Absolute distance between the radius and ulna decreased with age, while their relative separation increased due to positional shifts. The PCA-based model achieved superior prediction accuracy (mean RMSE: 2.0 mm) compared to PLSR (3.5 mm). Morphological prediction accuracy was lower than that of single-object SSMs (mean RMSE: 2.4 and 1.7 mm vs. 0.9 and 1.0 mm). However, the multi-object model preserved inter-bone spatial relationships with good agreement longitudinally and reasonable consistency in transverse and sagittal dimensions.

Conclusion
While the multi-object SSM of the forearm does not outperform single-object models in predicting bone morphology, it enables combined modeling of morphology and spatial alignment. This approach provides additional insights into coordinated forearm development and may support future clinical applications in growth assessment and surgical planning.