New regression equation for predicting post-treatment lower incisor position based on the pretreatment thickness of alveolar housing.

Abstract

Background: A regression model was formulated to assess the final lower incisor position based on its pretreatment alveolar bone housing. The objective of the study was to determine and quantify the thickness of alveolar bone in the mandibular incisor region using lateral cephalograms in skeletal Class I, Class II, and Class III patients. Formulate a calculated regression model on the final lower incisor based on its alveolar bone housing.

Methods: A retrospective analysis was conducted on 99 lateral cephalograms from patients with skeletal Class I, Class II, and Class III malocclusions. Digital tracing was performed to measure pretreatment alveolar bone thickness, including labial and lingual cortical thickness and alveolar spongiosa. A multivariate linear regression analysis was used to frame the equation. A one-way ANOVA and post hoc Scheffe tests were used to compare these variables across different skeletal classes and growth patterns.

Results: The regression analysis identified pretreatment incisor mandibular plane angle (IMPA) (β = 0.33; P = 0.011) and pretreatment lingual cortical thickness (β = -7.15; P = 0.001) as significant predictors of post-treatment IMPA and a regression equation to predict the post-treatment IMPA was developed. The skeletal Class I patients with average growth patterns exhibited greater labial and lingual cortical thickness than other classes and growth patterns.

Conclusions: A new regression model has been developed to predict post-treatment lower incisor position based on pretreatment alveolar housing. This model can enhance treatment planning and stability by accounting for individual anatomical variations. Clinicians should consider planning the post-treatment lower incisor position for a stable and successful treatment outcome.