ArticleBMC oral health2025
Hierarchical clustering analysis & machine learning models for diagnosing skeletal classes I and II in German patients.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Applying Automated Artificial Intelligence Models on Lateral Cephalometric Parameters to Accurately Classify Arab Orthodontic Patient Patterns.Clinical and experimental dental research · 2026Article
- Cross-Sectional Observational Study of the Differences in Cephalometric Parameters in German Class I/II Orthodontic Patients.International journal of dentistry · 2025Article
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11 authors.
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Abstract
backgroundClassification is one of the most common tasks in artificial intelligence (AI) driven fields in dentistry and orthodontics. The AI abilities can significantly improve the orthodontist's critical mission to diagnose and treat patients precisely, promptly, and efficiently. Therefore, this study aims to develop a machine-learning model to classify German orthodontic patients as skeletal class I or II based on minimal cephalometric parameters. Eventually, clustering analysis was done to understand the differences between clusters within the same or different skeletal classes.
methodsA total of 556 German orthodontic patients were classified into skeletal class I (n = 210) and II (n = 346) using the individualized ANB. Hierarchical clustering analysis used the Euclidean distances between data points and Ward's minimum variance method. Six machine learning models (random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA), classification and regression trees (CART), and General Linear Model (GLM)) were evaluated considering their accuracy, reliability, sensitivity, and specificity in diagnosing skeletal class I and II.
resultsThe clustering analysis results showed the power of this tool to cluster the results into two-three clusters that interestingly varied significantly in many cephalometric parameters, including NL-ML angle, NL-NSL angle, PFH/AFH ratio, gonial angle, SNB, Go-Me (mm), Wits appraisal, ML-NSL, and part of the dental parameters. The CART model achieved 100% accuracy by considering all cephalometric and demographic variables, while the KNN model performed well with three input parameters (ANB, Wits, SNB) only.
conclusionsThe KNN model with three key variables demonstrated sufficient accuracy for classifying skeletal classes I and II, supporting efficient and still personalized orthodontic diagnostics and treatment planning. Further studies with balanced sample sizes are needed for validation.
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