ArticleChildren (Basel, Switzerland)2024
Machine Learning Algorithms for the Diagnosis of Class III Malocclusions in Children.
Article in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Three Data-Driven Lip Soft-Tissue Phenotypes and Their Dento-Skeletal Associations in Orthodontic Patients: An Exploratory Unsupervised Learning Study.Orthodontics & craniofacial research · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.Scientific reports · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
Artificial intelligence has been applied to medical diagnosis and decision-making but it has not been used for classification of Class III malocclusions in children.
objectiveThis study aims to propose an innovative machine learning (ML)-based diagnostic model for automatically classifies dental, skeletal and functional Class III malocclusions.
methodsThe collected data related to 46 cephalometric feature measurements from 4-14-year-old children (
resultsThe top five models ranked by their area under the curve (AUC) values were: GPR (0.879), RBF SVM (0.876), QDA (0.876), Linear SVM (0.875) and L2 logistic (0.869). The DeLong test showed no statistical difference between GPR and the other models (
conclusionsOur findings suggest that ML models based on cephalometric data could effectively assist dentists to classify dental, functional and skeletal Class III malocclusions in children. In addition, features such as SN_GoMe, U1_NA and Overjet can as important indicators for predicting the severity of Class III malocclusions.
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Registered trials
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