Journal of International Society of Preventive & Community Dentistry · 2025
Applying Machine-Learning Models on Lateral Cephalogram Parameters to Classify Skeletal Classes I and III Arab Orthodontic Patients, Instead of Individualized Parameters.
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Midlej Kareem, Zohud Osayd, Masarwa Samir, Paddenberg-Schubert Eva, Krohn Sebastian, Proff Peter, Watted Nezar, Iraqi Fuad A
Аннотация
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 evaluated utilizing a 10-fold resampling technique. This study compared full-feature models with those using limited parameter sets, such as the Wits index and Sella-Nasion-B point angle measurement. Additionally, regression models were constructed to predict ANB angle using Wits, age, and gender.
RESULTS: Full-feature models achieved up to 97% accuracy, whereas reduced models maintained high performance (up to 91%) using Wits, S-N-B, and S-N-Pg angles. A marked positive association (r = 0.55, P < 0.01) was found shared by Wits and ANB in class III patients. The best regression model (R 2 = 0.57) predicted ANB using the formula: ANB = 4.37 + (0.47 × Wits) + (1.07 × gender) + (0.04 × age).
CONCLUSIONS: ML models can effectively classify skeletal classes I and III malocclusions in Arab subjects using basic cephalometric data, offering a reliable alternative to individualized assessment tools.
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 evaluated utilizing a 10-fold resampling technique. This study compared full-feature models with those using limited parameter sets, such as the Wits index and Sella-Nasion-B point angle measurement. Additionally, regression models were constructed to predict ANB angle using Wits, age, and gender.
RESULTS: Full-feature models achieved up to 97% accuracy, whereas reduced models maintained high performance (up to 91%) using Wits, S-N-B, and S-N-Pg angles. A marked positive association (r = 0.55, P < 0.01) was found shared by Wits and ANB in class III patients. The best regression model (R 2 = 0.57) predicted ANB using the formula: ANB = 4.37 + (0.47 × Wits) + (1.07 × gender) + (0.04 × age).
CONCLUSIONS: ML models can effectively classify skeletal classes I and III malocclusions in Arab subjects using basic cephalometric data, offering a reliable alternative to individualized assessment tools.