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

Abstract

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. Model performance was evaluated using mean absolute error (MAE), intraclass correlation coefficients (ICCs), and clinical thresholds (2 mm or 2°). Results: MAEs for skeletal measurements ranged from 1.36° (maxilla to cranial base angle) to 4.12 mm (mandibular length), and for dental measurements from 1.26 mm (lower incisor position) to 5.40° (upper incisor inclination). ICCs indicated moderate to excellent agreement between actual and predicted values. The highest prediction accuracy within the 2 mm or 2° clinical thresholds was achieved for maxilla to cranial base angle (80%), lower incisor position (75%), and maxilla to mandible angle (70%). Pre-pubertal measurements and sex consistently emerged as the most important predictive factors. Conclusions: ML models demonstrated the ability to predict post-pubertal values for maxilla to cranial base, mandible to cranial base, maxilla to mandible angles, upper and lower incisor positions, and upper face height with a clinically acceptable margin of 2 mm or 2°. Prediction accuracy was higher for skeletal relationships compared to dental relationships over the 8-year growth period. Pre-pubertal values of the measurements and sex emerged consistently as the most important predictors of the post-pubertal values.