Automatic Detection of Third Molar Tooth Development Stages in Panoramic Radiographs for Dental Age Assessment Using Faster R-CNN

Automatic dental age estimation in deep learning applications relies on accurate assessment of tooth development stage (TDS) and regions of interest (ROIs). Since dental development is population-specific, a recent study showed that a two-step approach using an ethnicity-specific reference dataset can improve explainability, transferability across populations, and agreement with chronological age. Therefore, this study aims to investigate whether the proposed population-aware framework can be extended toward greater automation by using Faster Region-Based Convolutional Network (R-CNN) for third-molar detection. A total of 1110 digital panoramic radiographs and 1980 labeled ROIs were used to train and validate four pretrained backbones: AlexNet, DenseNet-201, GoogLeNet, and VGG-16. DenseNet-201 achieved the best training performance and overall validation performance (accuracy = 95% and marco recall = 0.75), followed by GoogLeNet (84.7% and 0.80), VGG-16 (86.6% and 0.75), and AlexNet (79.8% and 0.58). Validation results showed relatively high recall across most TDS stages, whereas precision was lower because of false-positive (FP) detections. Performance was only minimally affected by the intersection-over-union (IoU) threshold, suggesting stable localization behavior. The trained Faster R-CNN models provide a clinical-oriented assistive tool for automatic third-molar detection and TDS classification, serving as an intermediate component that can potentially support more efficient ethnicity-specific dental age estimation in future clinical practice, forensic investigations, and pediatric dentistry.