Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans

Orhan K., BAYRAKDAR İ. Ş., Ezhov M., Kravtsov A., ÖZYÜREK T.

INTERNATIONAL ENDODONTIC JOURNAL, cilt.53, sa.5, ss.680-689, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53 Sayı: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1111/iej.13265
  • Dergi Adı: INTERNATIONAL ENDODONTIC JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.680-689
  • Anahtar Kelimeler: artificial intelligence, cone-beam computed tomography, deep learning, periapical pathology, CONVOLUTIONAL NEURAL-NETWORKS, APICAL PERIODONTITIS, DIAGNOSTIC-ACCURACY, FRACTURE DETECTION, ROOT RESORPTION, DEEP, RADIOGRAPHY, TEETH, CLASSIFICATION, LESIONS
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Aim To verify the diagnostic performance of an artificial intelligence system based on the deep convolutional neural network method to detect periapical pathosis on cone-beam computed tomography (CBCT) images.