Automated classification of skeletal malocclusion in German orthodontic patients.

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 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.

RESULTS

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.

CONCLUSIONS

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.

CLINICAL RELEVANCE

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.