An interview with Sylvia Frazier-Bowers

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. 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.