Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images


DUMAN Ş. B., ÇELİK ÖZEN D., BAYRAKDAR İ. Ş., Baydar O., Alhaija E. S. A., Helvacioğlu Yiğit D., ...Daha Fazla

Odontology, cilt.112, sa.2, ss.552-561, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 112 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s10266-023-00864-3
  • Dergi Adı: Odontology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MEDLINE
  • Sayfa Sayıları: ss.552-561
  • Anahtar Kelimeler: Cone beam computed tomography, Deep learning, Second mesiobuccal canals, YOLO
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients’ cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians’ time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.