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, vol.53, no.5, pp.680-689, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.1111/iej.13265
  • Journal Name: INTERNATIONAL ENDODONTIC JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, MEDLINE
  • Page Numbers: pp.680-689
  • Keywords: 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
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

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.