AI-Augmented 3D Morphometric Modeling for Growth-Responsive Orthodontic Design in Pediatric Dentistry

Pediatric orthodontics increasingly leverages computational modeling to predict craniofacial growth and improve appliance precision. Traditional orthodontic planning often relies on empirical growth charts that overlook individual morphometric variation. This research introduces an AI-augmented 3D morphometric modeling framework for the simulation and design of growth-responsive orthodontic appliances in children and adolescents.

Three-dimensional craniofacial scans (CBCT datasets, n = 400, ages 7–16) were processed through a convolutional neural network (CNN) integrated with geometric morphometrics to capture craniofacial growth vectors. The AI model learned spatiotemporal deformation patterns across developmental stages, identifying regions of rapid skeletal remodeling. Growth projection algorithms were then applied to simulate the impact of orthodontic forces on evolving dentofacial structures.

Results revealed that the AI model achieved 94.2% accuracy in predicting growth trajectories compared to longitudinal validation data. Predicted deformation fields enabled digital design of adaptive orthodontic aligners, which incorporated adjustable stress-distribution zones based on individual growth potential. Finite element analysis confirmed that growth-responsive aligners reduced stress concentration on unerupted molars by 31%, improving biomechanical harmony.

This study establishes a digital-twin framework that integrates morphometric intelligence, growth modeling, and biomechanical simulation for next-generation pediatric orthodontics. By merging AI analytics with personalized 3D geometry, orthodontic treatment can evolve toward non-invasive, self-adjusting systems that adapt to developmental dynamics. This in-silico paradigm holds promise for precision pediatric dentistry, reducing overcorrection risk and enhancing long-term skeletal stability.