Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

ABSTRACT: The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations between various anatomic structures. The primary outcome of this prospective cross-sectional study was developing a machine learning model for classifying patients as skeletal class I and III. Furthermore, the investigation intended to compare cephalometric variables between skeletal class I and III as well as between age and sex-specific subgroups to analyse correlations between cephalometric parameters and to perform Principal Component Analysis (PCA) to identify the most important variables contributing to skeletal class I and III variances.

SUBMITTER: Paddenberg-Schubert E 

PROVIDER: S-EPMC11994767 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature