Machine Learning–Driven Evaluation of Airway Morphodynamics in Orthodontic Treatment Planning

Abstract

Accurate assessment of airway morphodynamics is critical in orthodontic treatment planning, as alterations in airway structurecan influence both functional outcomes and patient health. Traditional evaluation methods are often time-consuming andlimited by subjective interpretation. This study explores a machine learning–driven approach to analyze airway morphologyusing three-dimensional imaging data. By extracting quantitative features such as airway volume, cross-sectional area, andshape metrics from CBCT scans, predictive models were developed to assess airway changes associated with orthodonticinterventions. The proposed framework demonstrated high accuracy in identifying clinically significant morphodynamic variations,offering a data-driven tool to enhance individualized treatment planning. Integration of machine learning in airwayevaluation promises to improve diagnostic precision and optimize orthodontic outcomes.