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 de

SUBMITTER: Myers M 

PROVIDER: S-EPMC11806318 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature