Accuracy of deep learning-based upper airway segmentation.

Journal: Journal of stomatology, oral and maxillofacial surgery
PMID: 39244033

Abstract

INTRODUCTION: In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method.

Authors

  • Yağızalp Süküt
    Department of Orthodontics, Gülhane Faculty of Dentistry, University of Health Sciences, Ankara 06010, Turkey. Electronic address: yagizalpsukut@gmail.com.
  • Ebru Yurdakurban
    Department of Orthodontics, Gulhane Faculty of Dental Medicine, University of Health Sciences, Ankara, Türkiye.
  • Gökhan Serhat Duran
    Department of Orthodontics, Gulhane Faculty of Dental Medicine, University of Health Sciences, Ankara, Turkey.

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