Şükriye Türkoğlu Kayacı, Hamza Osman İlhan, Görkem Serbes, Hakan Arslan
YOLOv8-based pose and segmentation models demonstrated technical feasibility for automating anatomical measurement extraction required for the Cameriere European dental age estimation method. Because dental age was not calculated in this first-stage analysis, the findings should be interpreted as measurement-level validation rather than complete dental age-estimation accuracy. Further validation is required to integrate AI-derived measurements into the Cameriere European formula and to compare AI-assisted dental age estimates with manual Cameriere-based assessment and chronological age.
The aim of this study was to develop a convolutional neural network (CNN)-based end-to-end learning architecture to predict the root development stages of permanent first molar teeth using panoramic radiographs. A dataset of 1629 first molar images was labeled according to the Cvek classification and organized into five subsets (DB-1 to DB-5) based on root development stages and apical foramen status. Teeth patches were cropped using the YOLO approach, and stage prediction was performed with VGG-19, InceptionV3, and EfficientNet-B3 models optimized with the Adamax optimizer at a learning rate of $$10^{-3}$$ . The proposed method achieved high precision (98.4%) and recall (97.6%) in detecting first molar teeth. Classification performance reached average accuracies of 64.21% for DB-1, 62.66% for DB-2, and 69.64% for DB-3. For apical foramina classification, an accuracy of 84.57% was obtained in DB-4, which further improved to 94.89% in DB-5. These findings highlight the potential of CNN-based approaches in dental diagnostics, providing clinicians with an effective tool for assessing root development and supporting treatment planning.