Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.

Objective: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.

Materials And Methods: Cephalometric radiographs from 301 subjects, taken at pre-pubertal (T1, age 11) and post-pubertal stages (T2, age 18), were analysed. Three ML models-Lasso regression, Random Forest, and Support Vector Regression (SVR)-were trained on a subset of 240 subjects, while 61 subjects were used for testing.