Bayesian networks analysis of malocclusion data
Bayesian networks describe the evolution of orthodontic features on patients receiving treatment versus no treatment for malocclusion.
M. Scutari, P. Auconi, G. Caldarelli, L. Franchi
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 expansion 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.
More in Mathematical medicine
Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.
Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.