| Yazarlar (4) |
Doç. Dr. Yağız ÖZBAY
Türkiye |
Dr. Öğr. Üyesi Adem PEKİNCE
Karabük Üniversitesi, Türkiye |
Buse Yaren Kazangirler
Türkiye |
Doç. Dr. Caner ÖZCAN
Karabük Üniversitesi, Türkiye |
| Özet |
| The study evaluated the diagnostic performance of an artificial intelligence system to detect separated endodontic instruments on periapical radiograph radiographs. Three hundred seven periapical radiographs were collected and divided into 222 for training and 85 for testing to be fed to the Mask R-CNN model. Periapical radiographs were assigned to the training and test set and labelled on the DentiAssist labeling platform. Labelled polygonal objects had their bounding boxes automatically generated by the DentiAssist system. Fractured instruments were classified and segmented. As a result of the proposed method, the mean average precision (mAP) metric was 98.809%, the precision value was 95.238, while the recall reached 98.765 and the f1 score 96.969%. The threshold value of 80% was chosen for the bounding boxes working with the Intersection over Union (IoU) technique. The Mask R-CNN distinguished separated endodontic instruments on periapical radiographs. |
| Anahtar Kelimeler |
| artificial intelligence | root canal treatment | separated endodontic instrument |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | AUSTRALIAN ENDODONTIC JOURNAL |
| Dergi ISSN | 1329-1947 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q3 |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2024 |
| Cilt No | 50 |
| Sayı | 1 |
| Sayfalar | 131 / 139 |
| Doi Numarası | 10.1111/aej.12822 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/abs/10.1111/aej.12822 |
| Atıf Sayıları | |
| WoS | 5 |
| Google Scholar | 8 |
