Detection of the separated endodontic instrument on periapical radiographs using a deep learning‑based convolutional neural network algorithm
Yazarlar (4)
Doç. Dr. Yağız ÖZBAY Karabük Üniversitesi, Türkiye
Buse Yaren Tekin
Karabük Üniversitesi, Türkiye
Karabük Üniversitesi, Türkiye
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Adem PEKİNCE Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Australian Endodontic Journal (Q3) | ||
| Dergi ISSN | 1329-1947 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | 50 / 1 / 131–139 | DOI | 10.1111/aej.12822 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/abs/10.1111/aej.12822 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| 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 … |
| Anahtar Kelimeler |
| artificial intelligence | root canal treatment | separated endodontic instrument |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 9 |
| Scopus | 13 |
| Google Scholar | 18 |