Clinically interpretable deep learning for pediatric dental age estimation with explainable AI

Chronological age estimation is essential for forensic science, law enforcement, and treatment planning. Dental orthopantomography (OPG) is widely used for this purpose, yet most conventional approaches remain rule based, slow, examiner dependent, and sensitive to image quality and dental variations. Pediatric OPGs from children aged 5–15 years were used to train and evaluate five deep learning networks, namely ConvNeXt-Tiny, ResNet-50, EfficientNet-B0, DenseNet-121 and Xception. With these models, ensemble strategies were developed and evaluated. Model performances were assessed using MAE, RMSE and R2. Additionally, explainable AI (XAI) heatmaps were generated to visualize and examine age informative regions. The proposed median ensemble achieved the best performance with an MAE of 0.616 years (≈ 7.4 months), outperforming all single networks. XAI analyses indicated that the models based their predictions on clinically plausible dental regions, with Xception providing the most precise and concentrated attributions. This study demonstrates that the median ensemble deep learning approach provides robust and examiner-independent pediatric age estimation from OPGs. This framework significantly reduces prediction errors compared with individual models. Furthermore, the integration of XAI-based visual explanations enhances interpretability, positioning this approach as a valuable decision support tool for forensic and dental practice.