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

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. Setting and Sample Population Archives of the CBCT images were reviewed, and the data of 306 subj...

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Veröffentlicht in:	Orthodontics & craniofacial research Jg. 24; H. S2; S. 117 - 123
Hauptverfasser:	Sin, Çağla, Akkaya, Nurullah, Aksoy, Seçil, Orhan, Kaan, Öz, Ulaş
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Wiley Subscription Services, Inc 01.12.2021
Schlagworte:	Algorithms Artificial Intelligence Computed tomography Cone-Beam Computed Tomography Deep Learning Humans Image processing Image Processing, Computer-Assisted Itk protein Learning algorithms Machine learning Neural networks pharyngeal airway Pharynx Respiratory tract Retrospective Studies Segmentation Spiral Cone-Beam Computed Tomography cone-beam computed tomography deep learning pharyngeal airway artificial intelligence
ISSN:	1601-6335, 1601-6343, 1601-6343
Online-Zugang:	Volltext
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