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

Abstract

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. Subjects were assigned to either a surgical or nonsurgical group depending on the treatment received. Treatment outcome was determined by facial profile and clinical photographs. Log binomial models were used for statistical analysis.

Results: Subtype 1 (mandibular prognathic) patients were 3.5 × more likely to undergo orthognathic surgery than subtypes 2/3 (maxillary deficient) and 5.3 × more likely than 4/5 (combination). Subtype 1 patients were also 1.5 × more likely to experience treatment failure than subtypes 2/3 (maxillary deficient) and 4/5 (combination).

Conclusions: This assessment of a systematic method to characterize patients with Class III malocclusion into subtypes revealed that subtype 1 (mandibular prognathic) showed a likelihood to undergo orthognathic surgery while subtypes 2/3 experienced significantly lower treatment failure (in response to orthodontics alone). Further refinement of the equation may yield a reliable prediction model for earlier identification of surgical patients and also provide predictive power of Class III treatment outcomes.