Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.

Journal: Dento maxillo facial radiology
Published Date: Apr 29, 2024

Abstract

OBJECTIVES: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

Authors

  • İbrahim Şevki Bayrakdar
    Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • Nermin Sameh Elfayome
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, 12613, Egypt.
  • Reham Ashraf Hussien
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, 12613, Egypt.
  • Ibrahim Tevfik Gulsen
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, Antalya, 07425, Turkey.
  • Alican Kuran
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli 41190, Turkey.
  • Ihsan Gunes
    Open and Distance Education Application and Research Center, Eskisehir Technical University, Eskisehir, 26555, Turkey.
  • Alwaleed Al-Badr
    Restorative Dentistry, Riyadh Elm University, Riyadh, 13244, Saudi Arabia.
  • Özer Çelik
    Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • Kaan Orhan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Dentomaxillofacial Radiologist, Ankara University, Ankara, Turkey.

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