A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images.

Journal: Orthodontics & craniofacial research
Published Date: Mar 8, 2021

Abstract

OBJECTIVES: This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.

Authors

  • Çağla Sin
    Faculty of Dentistry, Department of Orthodontics, Near East University, Mersin10, Turkey.
  • Nurullah Akkaya
    Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
  • Seçil Aksoy
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Near East University, Mersin10, Turkey.
  • Kaan Orhan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Dentomaxillofacial Radiologist, Ankara University, Ankara, Turkey.
  • Ulaş Öz
    Faculty of Dentistry, Department of Orthodontics, Near East University, Mersin10, Turkey.

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