ArticleDiagnostics (Basel, Switzerland)2023
Automated Sagittal Skeletal Classification of Children Based on Deep Learning.
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 14 papers.
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14 citing papers in PubMed.
- Artificial intelligence for skeletal classification in orthodontics: A systematic review and meta-analysis of lateral cephalometric studies.Journal of Taibah University Medical Sciences · 2026Review
- A Novel Multimodal Deep Image Analysis Model for Predicting Extraction/Non-Extraction Decision.Orthodontics & craniofacial research · 2026Article
- Performance evaluation of deep learning models for overbite classification on cephalometric radiographs.European oral research · 2026Article
- MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.Scientific reports · 2025Article
- Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Automated classification of skeletal malocclusion in German orthodontic patients.Clinical oral investigations · 2025Article
- Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.Scientific reports · 2025Article
- Federated Learning-Based CNN Models for Orthodontic Skeletal Classification and Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Development of a diagnostic classification model for lateral cephalograms based on multitask learning.BMC oral health · 2025Article
- Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.Biomedical engineering online · 2025Article
- Machine Learning Algorithms for the Diagnosis of Class III Malocclusions in Children.Children (Basel, Switzerland) · 2024Article
- Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning.Medical & biological engineering & computing · 2023Article
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8 authors.
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Abstract
Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the automatic detection of malocclusions in children has not been reported. Therefore, the aim of this study was to develop a deep learning-based method for automatic classification of the sagittal skeletal pattern in children and to validate its performance. This would be the first step in establishing a decision support system for early orthodontic treatment. In this study, four different state-of-the-art (SOTA) models were trained and compared by using 1613 lateral cephalograms, and the best performance model, Densenet-121, was selected was further subsequent validation. Lateral cephalograms and profile photographs were used as the input for the Densenet-121 model, respectively. The models were optimized using transfer learning and data augmentation techniques, and label distribution learning was introduced during model training to address the inevitable label ambiguity between adjacent classes. Five-fold cross-validation was conducted for a comprehensive evaluation of our method. The sensitivity, specificity, and accuracy of the CNN model based on lateral cephalometric radiographs were 83.99, 92.44, and 90.33%, respectively. The accuracy of the model with profile photographs was 83.39%. The accuracy of both CNN models was improved to 91.28 and 83.98%, respectively, while the overfitting decreased after addition of label distribution learning. Previous studies have been based on adult lateral cephalograms. Therefore, our study is novel in using deep learning network architecture with lateral cephalograms and profile photographs obtained from children in order to obtain a high-precision automatic classification of the sagittal skeletal pattern in children.
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