Improving Chronological Age Estimation in Children Using the Demirjian Method Enhanced with Transformer and...

This study presents a two-phase methodology for estimating chronological age in children using panoramic dental images and deep learning-based feature extraction. The dataset comprised 626 panoramic radiographs from children aged 6.0 to 13.8 years (320 males, 306 females; mean age = 9.88 years). Two expert dentists annotated each radiograph according to the Demirjian stages of seven mandibular teeth. In the first phase, three architectures-ResNet-18, EfficientNetV2-M, and Swin V2 Base-were trained separately for males and females to extract high-dimensional feature representations. Images were preprocessed via intensity quantization, histogram equalization, segmentation, and resizing to standardized 224 × 224 pixel inputs. From the fully connected layer of the Swin V2 Base model, 512 features were extracted for each tooth, and the concatenation of seven teeth yielded a 3584-dimensional feature vector per subject. These feature vectors were then used for regression analysis to predict chronological age on a day-level scale. In the second phase, nine machine learning regression models-LightGBM, RandomForest, ExtraTrees, GradientBoosting, XGBoost, KNN, SVR, MLP, and Gaussian Process Regression-were trained using the extracted features. Pairwise t-test analysis revealed ExtraTrees as the most statistically significant model. For this model, RMSE and MAE were 6.98 and 5.18 months for females, and 6.55 and 5.01 months for males. SHAP-based analysis highlighted the second molar (M2) and first premolar (P1) as the most influential features for females, and the first premolar (P1) and second molar (M2) for males. This automated pipeline enhances age prediction accuracy, reduces observer variability, and provides a reliable tool for clinical and forensic dental age estimation. Future work will explore dataset expansion, multimodal integration, and refined model architectures.