  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.
