Journal of the World federation of orthodontists · 2025
Deep learning-based system for automated staging of lower molar maturation.
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Biskupovic Fernando, Rosenberg Flavia, Searle Luz María, Ramírez Pamela, Larrañaga María Jesús, Maldonado Sebastián, Vairetti Carla, Oyonarte Rodrigo
Аннотация
BACKGROUND: Assessing a patient's maturation status is essential for treatment planning in dentofacial orthopedics. Dental development, as classified by Demirjian's method into eight stages, is a reliable indicator of skeletal maturity relative to the pubertal growth spurt. Automating this assessment may improve efficiency by reducing subjectivity and supporting timely orthodontic interventions.
METHODS: A cross-sectional study was conducted using segmented panoramic radiographs to classify the maturation stages of lower second and third molars. These classifications served as training data for machine learning models using four convolutional neural network (CNN) architectures: Xception, ResNet, MobileNet, and Inception. Model performance was evaluated on three datasets: second and third molars combined (ST), second molars only (S), and third molars only (T). Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize model attention.
RESULTS: A total of 1805 images were analyzed. Inception achieved the best performance in both the ST dataset (accuracy 0.96, precision 0.86, recall 0.85, and F1 score 0.85) and the S dataset (accuracy 0.98, precision 0.92, recall 0.91, and F1 score 0.89). For the T dataset, ResNet performed the best (accuracy 0.96, precision 0.94, recall 0.95, and F1 score 0.81). Inter-examiner agreement was high, with a mean kappa coefficient of 0.94. Grad-CAM heat maps confirmed that the model focused on relevant dental structures.
CONCLUSIONS: The proposed deep learning system, especially the Inception model, demonstrated high accuracy and strong agreement with experts when classifying dental maturation stages. These findings support its use as a complementary diagnostic tool to aid clinical decision-making in growth assessment.
METHODS: A cross-sectional study was conducted using segmented panoramic radiographs to classify the maturation stages of lower second and third molars. These classifications served as training data for machine learning models using four convolutional neural network (CNN) architectures: Xception, ResNet, MobileNet, and Inception. Model performance was evaluated on three datasets: second and third molars combined (ST), second molars only (S), and third molars only (T). Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize model attention.
RESULTS: A total of 1805 images were analyzed. Inception achieved the best performance in both the ST dataset (accuracy 0.96, precision 0.86, recall 0.85, and F1 score 0.85) and the S dataset (accuracy 0.98, precision 0.92, recall 0.91, and F1 score 0.89). For the T dataset, ResNet performed the best (accuracy 0.96, precision 0.94, recall 0.95, and F1 score 0.81). Inter-examiner agreement was high, with a mean kappa coefficient of 0.94. Grad-CAM heat maps confirmed that the model focused on relevant dental structures.
CONCLUSIONS: The proposed deep learning system, especially the Inception model, demonstrated high accuracy and strong agreement with experts when classifying dental maturation stages. These findings support its use as a complementary diagnostic tool to aid clinical decision-making in growth assessment.