Applying Machine-Learning Models on Lateral Cephalogram Parameters to Classify Skeletal Classes I and III Arab Orthodontic Patients, Instead of Individualized Parameters.

ABSTRACT:

Aims

The sagittal skeletal correlation involving the maxilla and mandible is commonly assessed utilizing the A point, Nasion, B point (ANB) angle and Wits appraisal. Recently, ethnicity-specific diagnostic values have been proposed to improve accuracy. This study investigates whether machine learning (ML) models can classify Arab orthodontic cases identified with skeletal class I or class III determined only by standard skeletal measurement indicators, without relying on individualized equations.

Materials and methods

Lateral cephalograms from 422 Arab orthodontic patients were analyzed in this study. Five supervised ML algorithms-linear discriminant analysis, support vector machine, K-nearest neighbor, random forest, and Classification and Regression tree-were developed and

SUBMITTER: Midlej K 

PROVIDER: S-EPMC12617986 | biostudies-literature | 2025 Sep-Oct

REPOSITORIES: biostudies-literature