Deep-learning based fusion of spatial relationship classification between mandibular third molar and inferior alveolar nerve using panoramic radiograph images

Kumbasar N., Güller M. T., MİLOĞLU Ö., ORAL E. A., ÖZBEK İ. Y.

Biomedical Signal Processing and Control, cilt.100, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 100
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.bspc.2024.107059
  • Dergi Adı: Biomedical Signal Processing and Control
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, INSPEC
  • Anahtar Kelimeler: Deep learning, Inferior alveolar nerve, Late fusion, Majority voting, Mandibular third molar, Panoramic radiograph
  • Atatürk Üniversitesi Adresli: Evet

Özet

It is crucial for clinicians to have a prior knowledge of spatial relationship between impacted mandibular third molar tooth (MM3) and inferior alveolar nerve (IAN) before an extraction procedure. This relationship may exist in four spatial forms in terms of IAN position relative to MM3 although it has not been studied extensively. To identify such relationship type, on the other hand, this study proposes a novel four-class classification framework utilizing fusion of AlexNet, VGG16, VGG19 deep learning methods using panoramic radiograph (PR) images. For this purpose, 546 PR images of impacted MM3, collected from 290 patients, were labeled by specialists using corresponding cone beam computed tomography (CBCT) images. The proposed network is trained and tested using 10 folds cross validation. Experimental studies were performed in different categories. In the first (MM3 and IAN are related/unrelated) an accuracy rate of 94.1% was obtained. In the following IAN resides on the lingual or vestibule (buccal) side of MM3 classification problem, test result of 80.6% accuracy was obtained. Finally, in the challenging four-class classification problem that includes unrelated, lingual, vestibule and other classes, an accuracy rate of 79.7% was achieved. Obtained results show that the proposed method not only presents state-of-the-art results but also suggests a new classification basis for the existing MM3-IAN relationship problem.