Dental age prediction from panoramic radiographs using machine learning techniques

Dental age (DA) estimation is a key diagnostic tool in pediatric dentistry, particularly when birth records are unavailable or unreliable. It guides decisions on growth assessment, orthodontic planning, and timing of interventions such as space maintenance or extractions. Unlike skeletal maturity, dental development is less affected by nutritional and environmental factors, making it a reliable marker of biological age. Conventional methods require expert interpretation and are prone to variability. There is growing interest in automated, objective approaches to streamline this process and enhance clinical utility. A total of 550 panoramic radiographs from children aged 3-14 years were labeled into 11 dental age groups based on the AAPD reference chart by two experienced pediatric dentists. Images with poor quality were excluded. The dataset was divided into training (80%) and validation (20%) sets, with data augmentation applied to the training set. The YOLOv11n-cls model, consisting of 86 layers and 1.54 million parameters, was trained for 30 epochs using the Ultralytics engine and AdamW optimizer. Model performance was evaluated using Top-1 and Top-5 accuracy on the validation set and tested on an independent set of 203 images. Grad-CAM was used for model interpretability. The model achieved 92.6% Top-1 and 99.5% Top-5 accuracy on the validation set. Performance on the test set remained high, with most misclassifications occurring between adjacent age groups. Grad-CAM visualizations showed attention to clinically relevant areas like erupting molars and root development. The findings support the high performance of DL, through YOLOv11 for pediatric age prediction. The AI tool enabled fast, accurate, and interpretable DA classification, making it a strong candidate for clinical integration as an adjunct tool into pediatric dental practice.