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Automated Sagittal Skeletal Classification of Children Based on Deep Learning
Lan Nan1, Min Tang1, Bohui Liang2
1College of Stomatology, Guangxi Medical University, Nanning 530021, China.
Diagnostics (Basel, Switzerland)
|May 27, 2023
Summary
A new deep learning method accurately classifies children's sagittal skeletal patterns using cephalograms and photos. This supports early diagnosis of malocclusions, benefiting pediatric orthodontic treatment.
Area of Science:
- Dentistry and Oral Health
- Artificial Intelligence in Medicine
- Pediatric Craniofacial Development
Background:
- Malocclusions, cranio-maxillofacial developmental deformities, are common in children.
- Early diagnosis and intervention are crucial for effective orthodontic treatment.
- Automated malocclusion detection using deep learning in children remains underexplored.
Purpose of the Study:
- To develop and validate a deep learning-based method for automatic classification of sagittal skeletal patterns in children.
- To establish a foundational decision support system for early orthodontic interventions.
- To address the novelty of applying deep learning to pediatric cephalometric data.
Main Methods:
- Trained and compared four state-of-the-art deep learning models using 1613 lateral cephalograms.
- Selected Densenet-121 as the best-performing model for further validation.
- Utilized transfer learning, data augmentation, and label distribution learning for model optimization.
- Input data included lateral cephalograms and profile photographs.
- Evaluated performance using five-fold cross-validation.
Main Results:
- The Convolutional Neural Network (CNN) model using lateral cephalometric radiographs achieved 83.99% sensitivity, 92.44% specificity, and 90.33% accuracy.
- The model using profile photographs achieved 83.39% accuracy.
- Incorporating label distribution learning improved accuracies to 91.28% (cephalograms) and 83.98% (photographs), while reducing overfitting.
- The Densenet-121 model demonstrated high precision in classifying pediatric sagittal skeletal patterns.
Conclusions:
- Deep learning models, particularly Densenet-121, can accurately classify pediatric sagittal skeletal patterns from lateral cephalograms and profile photographs.
- The developed method represents a significant advancement for automated malocclusion detection in children.
- This approach holds promise for developing decision support systems for timely orthodontic treatment in pediatric populations.
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