ArticleDiagnostics (Basel, Switzerland)2023
Classification of Maxillofacial Morphology by Artificial Intelligence Using Cephalometric Analysis Measurements.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development and External Validation of an Explainable AHP-ML Model for Orthodontic Tooth Extraction and Anchorage Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
- A YOLOv12-based approach for automatic detection of cephalometric landmarks on 2D lateral skull X-ray images.Scientific reports · 2026Article
- Evaluation of the Performance of an Artificial Intelligence-Based Classification Model for Pediatric Maxillofacial Morphology.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Optimizing LM-DRAMA parameters and non-local means filtering to improve small-lesion detectability in SiPM-based TOF breast PET.Physical and engineering sciences in medicine · 2025Article
- Automated classification of skeletal malocclusion in German orthodontic patients.Clinical oral investigations · 2025Article
- Hierarchical clustering analysis & machine learning models for diagnosing skeletal classes I and II in German patients.BMC oral health · 2025Article
- The wings of progress: technological and radiopharmaceutical innovations in nuclear medicine.European journal of nuclear medicine and molecular imaging · 2024Article
- Comparative evaluation of commercially available AI-based cephalometric tracing programs.BMC oral health · 2024Article
- Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.Diagnostics (Basel, Switzerland) · 2024Review
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
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Authors and funding
8 authors.
Funding
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Abstract
The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni's classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject's gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.
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