# Bayesian Networks Analysis of Malocclusion Data

## TL;DR

This study employs Bayesian networks to analyze and visualize the interactions among craniofacial features in Class III malocclusion patients, revealing differences in growth patterns and treatment effects.

## Contribution

It introduces a Bayesian network approach to model and test hypotheses about craniofacial feature interactions during growth and treatment.

## Key findings

- Untreated subjects develop different growth patterns compared to treated patients.
- Treatment mainly affects the maxillary length and maxilla-mandible relationship.
- Bayesian analysis confirms known hypotheses about craniofacial development.

## Abstract

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expantion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.