A machine learning approach to determine the prognosis of patients with Class III malocclusion.

Introduction: The conundrum of determining how to treat a patient with Class III malocclusion is significant, creating a burden on the patient and challenging the orthodontist. The objective of this study was to employ a statistical prediction model derived from our previous cephalometric data on 5 predominant subtypes of skeletal Class III malocclusion to test the hypothesis that Class III subtypes are associated with treatment modalities (eg, surgical vs nonsurgical) and treatment outcome.

Methods: Pretreatment lateral cephalometric records of 148 patients were digitized for 67 cephalometric variables, and measurements were applied to a mathematical equation to assign a Class III subtype.