End-to-end CNN-based detection of permanent first molars and prediction of root development stages from panoramic radiographs

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>10</mml:mn> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>3</mml:mn></mml:mrow> </mml:msup> </mml:math> . 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.