# Automated classification of skeletal malocclusion in German orthodontic patients

## TL;DR

This study shows that AI can accurately classify skeletal malocclusion in German orthodontic patients, potentially streamlining diagnostic workflows.

## Contribution

The study introduces AI models that achieve 100% accuracy in classifying skeletal classes using cephalometric data from German patients.

## Key findings

- Random forest models achieved 100% accuracy in classifying skeletal classes using all input parameters.
- Calculated_ANB was identified as the most important parameter for accurate classification.
- An artificial neural network achieved 95.31% accuracy with all parameters and 100% with Calculated_ANB alone.

## Abstract

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.

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 forest (RF), decision tree (DT), K-nearest neighbours (KNN), support vector machine (SVM), Gaussian naïve Bayes (NB), and multi class logistic regression (MCLR). Furthermore, an artificial neural network (ANN) was conducted.

1277 German patients presented skeletal class I (48.79%), II (27.56%) and III (23.64%). The best machine-learning model, which considered all input parameters, was RF with 100% accuracy, with Calculated_ANB being the most important (0.429). The model with Calculated_ANB only achieved 100% accuracy (KNN), but ANB alone was inappropriate (71–76% accuracy). The ANN with all parameters and Calculated_ANB achieved 95.31% and 100% validation-accuracy, respectively.

Machine- and deep-learning methods can correctly determine an individual’s skeletal class. Calculated_ANB was the most important among all input parameters, which, therefore, requires precise determination.

The AI methods introduced may help to establish digital and automated workflows in cephalometric diagnostics, which could contribute to the relief of the orthodontic practitioner.

The online version contains supplementary material available at 10.1007/s00784-025-06485-0.