Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans
INTERNATIONAL ENDODONTIC JOURNAL, cilt.53, sa.5, ss.680-689, 2020 (SCI-Expanded, Scopus)
- 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.