Evaluating the Predictive Potential of Patient-Specific Biomechanical Models in Class III Protraction Therapy

TL;DR

This study evaluates how well biomechanical models can predict outcomes in Class III protraction therapy, finding that they are not reliable without incorporating biological growth factors.

Contribution

The study is the first to validate the predictive accuracy of finite element models in Class III protraction therapy using patient-specific anatomical data.

Findings

FEM-predicted maxillary changes were about one-tenth of actual clinical changes.

Patient-specific geometrical factors had a greater impact on deformation than treatment type.

Current FEM models lack reliability in predicting clinical outcomes for growing Class III patients.

Abstract

Predicting treatment outcomes in Class III protraction therapy remains challenging. Although finite element analysis (FEA) helps in the study of biomechanics and planning of orthodontic treatment, its use in Class III protraction has mainly been in evaluating appliance designs rather than patient-specific anatomy. The predictive accuracy of FEA has not been validated in Class III protration therapy. In this study, ten patients (5 female, 5 male, aged 7–11 years) with Class III malocclusion received either facemask or mentoplate treatment. CT scans from four patients were used to construct simplified finite element models, and predictions were compared with one-year treatment outcomes from six additional patients. While stress patterns differed between treatments, patient-specific geometrical factors had a more significant impact on deformation than treatment type. FEM-predicted maxillary changes (mean: 0.352 ± 0.12 mm) were approximately one-tenth of actual changes (mean: 1.612 ± 0.64 mm), with no significant correlation. Current FEM approaches, though useful for understanding force distribution, cannot reliably predict clinical outcomes in growing Class III patients. The findings suggest that successful prediction models must incorporate biological and growth factors beyond pure biomechanics. Accurate prediction of treatment outcomes requires comprehensive models that integrate multiple biological and developmental factors.

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