Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images

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Syed A. Z., ÇELİK ÖZEN D., Abdelkarim A. Z., DUMAN Ş. B., BAYRAKDAR İ. Ş., DUMAN S., ...Daha Fazla

International Journal of Intelligent Systems, cilt.2023, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 2023
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1155/2023/4415970
  • Dergi Adı: International Journal of Intelligent Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients' cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model's robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.