Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography

Kadan E., Kiliç R., Miloğlu Ö., ÖZBEK İ. Y., Oral E.

Nigerian Journal of Clinical Practice, cilt.27, sa.5, ss.669-677, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 5
  • Basım Tarihi: 2024
  • Doi Numarası: 10.4103/njcp.njcp_220_24
  • Dergi Adı: Nigerian Journal of Clinical Practice
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MEDLINE
  • Sayfa Sayıları: ss.669-677
  • Anahtar Kelimeler: Artificial intelligence, deep learning, machine learning, maxillary molars, maxillary sinus, neural networks
  • Atatürk Üniversitesi Adresli: Evet

Özet

Background: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used. Aim: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT). Methods: A total of 2162 fMMT and sMMT were included in this retrospective study. The contact relationship of teeth with MSF was compared among DL methods. Results: DL methods, such as GoogLeNet, VGG16, VGG19, DarkNet19, and DarkNet53, were used to evaluate the contact relationship between MMT and MSF, and 85.89% accuracy was achieved by majority voting. In addition, 88.72%, 81.19%, 89.39%, and 83.14% accuracy rates were obtained in right fMMT, right sMMT, left fMMT, and left sMMT, respectively. Conclusion: DL models showed high accuracy values in detecting the relationship of fMMT and sMMT with MSF.