Artificial Intelligence-Driven Dental Age Estimation in Panoramic Radiographs via the Demirjian Method - Valizadeh, Solmaz; Moshfeghi, Mahkameh; Mirmohammadsadeghi, Hoori; Mozaffari, Mahdi (Journal of Dental School, 2025)
Objective(s): This research aimed to design and implement an artificial intelligence (AI) model for dental age estimation in panoramic radiographs using the Demirjian method. Accurate dental age estimation is crucial in forensic and clinical dentistry. Traditional methods for this purpose are typically time-consuming and require expertise, which increases the likelihood of human error. This study explored the potential of AI, particularly Vision Transformers (ViTs), to overcome these limitations. Methods: A number of 422 panoramic radiographs were analyzed, yielding 2,836 individual tooth images. The developmental stages of left mandibular teeth were determined according to the Demirjian method. Initially, 15% of the data was randomly separated for the test set. Subsequently, five-fold cross-validation was employed to partition the remaining data into training and validation sets. Three Convolutional Neural Network (CNN) models (ConvNeXt-Tiny, EfficientNet-V2-S, RegNet-Y-16GF) and three ViT models (ViT-B-16, Swin-V2-T, MaxVit-T) were trained using transfer learning. Model performance was evaluated using accuracy, precision, recall, and F1-score. The best-performing model, based on F1-score, was deployed in a web-based application. Mean Absolute Error (MAE) was used to assess the accuracy of AI-based age estimation compared to chronological age. Results: The Swin-V2-T model achieved the highest performance across all metrics (F1-score of 87%, Accuracy of 87.09%, Precision of 87.17%, and Recall of 87.09%). Analysis of AI-based age estimation accuracy revealed an MAE of 0.953 years. Conclusion: This study highlighted the applicability of AI, particularly ViT-based models, in automated dental age estimation using the Demirjian method. The developed AI model has the potential to streamline dental age assessments in both clinical and forensic settings, minimizing human error and enhancing efficiency.