ArticleHead & face medicine2024
Comparison of deep learning models to detect crossbites on 2D intraoral photographs.
Article in Head & face medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automated classification of sagittal, vertical, and transverse malocclusions from 3D intraoral scans via a multi-head CNN framework.Odontology · 2026Article
- Development of Leading Enhancement Assistive Planning: A three-dimensional malocclusion classification system for orthodontic treatment with clear aligners.Korean journal of orthodontics · 2026Article
- Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic Radiographs.Orthodontics & craniofacial research · 2026Article
- Comparing Deep Learning Models for Identifying Maxillary Transverse Deficiency from Intraoral Photographs.International dental journal · 2026Article
- Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN): Can large language models match the performance of orthodontists?Korean journal of orthodontics · 2026Article
- Multi-Class Malocclusion Detection on Standardized Intraoral Photographs Using YOLOv11.Dentistry journal · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs.
methodsBased on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs. lateral crossbite vs. frontal crossbite. The trained models comprised DenseNet, EfficientNet, MobileNet, ResNet18, ResNet50, and Xception.
findingsAmong the models, Xception showed the highest accuracy (98.57%) in the test dataset for classifying non-crossbite vs. crossbite images. When additionally distinguishing between lateral and frontal crossbites, average accuracy decreased with the DenseNet architecture achieving the highest accuracy among the models with 91.43% in the test dataset.
conclusionsConvolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
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