Deep learning for dental age assessment from panoramic radiographs: regression-based age estimation and threshold-based age classification

Deep learning has significantly advanced medical image analysis by enabling automated extraction of complex imaging features with high predictive performance. In forensic dental age assessment, however, most artificial intelligence studies focus on either chronological age estimation or legal age threshold classification, limiting direct comparisons between predictive tasks. This study aimed to evaluate deep learning models for both continuous age estimation and judicial age threshold classification using panoramic radiographs, assessing their performance, robustness and potential applicability as decision-support tools in forensic practice.

A dataset of 9,814 anonymised digital panoramic radiographs from individuals with known chronological age was retrospectively collected and standardised through image preprocessing. Transfer learning applied to convolutional neural network architectures to address two complementary prediction tasks: chronological age estimation as a regression problem and legal age threshold classification at 10, 12, 14, 16, 18 and 21 years as independent binary classification problems. Model performance was evaluated using mean absolute error (MAE), coefficient of determination (R²), median absolute error and mean absolute percentage error (MAPE) for regression, and accuracy, precision, recall and F1-score for classification.

The regression model achieved an MAE of 1.122 years, an R² of 0.917 and a MAPE of 7.67% on the independent test dataset, demonstrating good predictive performance across the analysed age range. Classification models achieved accuracy exceeding 90% with F1-scores above 0.90 for the 10-, 12-, 14- and 16-year legal thresholds. Lower predictive performance at the 18- and 21-year thresholds reflected increased biological variability and class imbalance associated with later stages of dental development.

Deep learning provides good predictive performance from panoramic radiographs for both continuous age estimation and legal age threshold classification. The complementary evaluation of regression and classification demonstrates the versatility of convolutional neural networks for forensic dental image analysis and supports their integration into trustworthy artificial intelligence systems for clinical and judicial decision support.