Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction
Clinical Oral Investigations · Nisan 2025
YÖKSİS Kayıtları
Automatic Mandibular Third Molar and Mandibular Canal Relationship Determination Based on Deep Learning Models for Preoperative Risk Reduction
Clinical Oral Investigations · 2025 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiClinical Oral Investigations
Yayın TarihiNisan 2025
Cilt / Sayfa29
Scopus ID2-s2.0-105000636253
Erişim🔓 Açık Erişim
Özet
Objectives: This 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 Methods: A 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. Results: MobileNet achieved the highest overall performance, with an accuracy of 99.44%. Xception and DenseNet201 also demonstrated strong classification capabilities, with accuracies of 98.74% and 98.73%, respectively. Conclusions: These results highlight the potential of deep learning models to automate and improve the accuracy and consistency of mandibular third molars and the mandibular canal relationship classifications. Clinical Relevance: The integration of such systems into clinical workflows could enhance surgical risk assessments, streamline diagnostics, and reduce reliance on manual analysis, particularly in resource-constrained settings. This study contributes to advancing the use of artificial intelligence in dental imaging, offering a promising avenue for safer and more efficient surgical planning.
Yazarlar (4)
1
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
2
Mediha Erturk
ORCID: 0000-0002-8530-9335
3
Melek Tassoker
ORCID: 0000-0003-2062-5713
4
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Cone beam computed tomography
Deep learning models
Dental imaging
Mandibular canal
Mandibular third molar
Medical image analysis
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey