Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction
Yazarlar (4)
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Arş. Gör. Mediha Erturk Necmettin Erbakan Üniversitesi, Türkiye
Melek Tassoker
Necmettin Erbakan Üniversitesi, Türkiye
Necmettin Erbakan Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Clinical Oral Investigations (Q1) | ||
| Dergi ISSN | 1432-6981 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2025 |
| Kabul Tarihi | 13-03-2025 | Yayınlanma Tarihi | 25-03-2025 |
| Cilt / Sayı / Sayfa | 29 / 4 / 203–220 | DOI | 10.1007/s00784-025-06285-6 |
| Makale Linki | https://doi.org/10.1007/s00784-025-06285-6 | ||
| UAK Araştırma Alanları |
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| Özet |
| ObjectivesThis study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this relationship is essential for preoperative planning, as improper assessment can lead to complications such as inferior alveolar nerve injury during extractions.Materials and MethodsA dataset of 305 cone-beam computed tomography scans, categorized into three classes (not contacted, nearly contacted, and contacted), was meticulously annotated and validated by maxillofacial radiology experts to ensure reliability. Multiple state-of-the-art convolutional neural networks, including MobileNet, Xception, and DenseNet201, were trained and evaluated. Performance metrics were analysed.ResultsMobileNet achieved the highest overall performance, with an accuracy of 99.44 … |
| Anahtar Kelimeler |
| Cone beam computed tomography | Deep learning models | Dental imaging | Mandibular canal | Mandibular third molar | Medical image analysis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 10 |
| Scopus | 13 |
| Google Scholar | 19 |