Detection of maxillary sinus pathologies using deep learning algorithms.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Published Date: May 20, 2025

Abstract

PURPOSE: Deep learning, a subset of machine learning, is widely utilized in medical applications. Identifying maxillary sinus pathologies before surgical interventions is crucial for ensuring successful treatment outcomes. Cone beam computed tomography (CBCT) is commonly employed for maxillary sinus evaluations due to its high resolution and lower radiation exposure. This study aims to assess the accuracy of artificial intelligence (AI) algorithms in detecting maxillary sinus pathologies from CBCT scans.

Authors

  • Ceren Aktuna Belgin
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Hatay Mustafa Kemal University, Hatay, Turkey.
  • Aida Kurbanova
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Near East University, Mersin10, 99138, Turkey.
  • Seçil Aksoy
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, 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.
  • Kaan Orhan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Dentomaxillofacial Radiologist, Ankara University, Ankara, Turkey.

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