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

Kayaci S. T., İLHAN H. O., SERBES G., Arslan H.

Scientific Reports, cilt.15, sa.1, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1038/s41598-025-22707-7
  • Dergi Adı: Scientific Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
  • Anahtar Kelimeler: Deep learning in dentistry, Permanent first molar, Regenerative endodontics, Root development stages, Transfer learning, YOLO algorithm
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.