Forecasting craniofacial growth in individuals with class III malocclusion by computational modelling.

AIM: To develop a mathematical model that adequately represented the pattern of craniofacial growth in class III subject consistently, with the goal of using this information to make growth predictions that could be amenable to longitudinal verification and clinical use.
MATERIALS AND METHODS: A combination of computational techniques (i.e. Fuzzy clustering and Network analysis) was applied to cephalometric data derived from 429 untreated growing female patients with class III malocclusion to visualize craniofacial growth dynamics and correlations. Four age groups of subjects were examined individually: from 7 to 9 years of age, from 10 to 12 years, from 13 to 14 years, and from 15 to 17 years.
RESULTS: The connections between pathway components of class III craniofacial growth can be visualized from Network profiles. Fuzzy clustering analysis was able to define further growth patterns and coherences of the traditionally reported dentoskeletal characteristics of this structural imbalance. Craniofacial growth can be visualized as a biological, space-constraint-based optimization process; the prediction of individual growth trajectories depends on the rate of membership to a specific 'winner' cluster, i.e. on a specific individual growth strategy. The reliability of the information thus gained was tested to forecast craniofacial growth of 28 untreated female class III subjects followed longitudinally.
CONCLUSION: The combination of Fuzzy clustering and Network algorithms allowed the development of principles for combining multiple auxological cephalometric features into a joint global model and to predict the individual risk of the facial pattern imbalance during growth.