Automated classification of skeletal malocclusion in German orthodontic patients.

ABSTRACT:

Objectives

Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differentiate between skeletal classes I, II, and III in German orthodontic patients. This prospective cross-sectional study aimed to develop machine- and deep-learning models for diagnosing their skeletal class based on the gold-standard individualised ANB of Panagiotidis and Witt.

Materials and methods

Orthodontic patients treated in Germany contributed to the study population. Pre-treatment cephalometric parameters, sex, and age served as input variables. Machine-learning models performed were linear discriminant analysis (LDA), random

SUBMITTER: Paddenberg-Schubert E 

PROVIDER: S-EPMC12325434 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature