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

Sin C., Akkaya N., Aksoy S., ORHAN K., Oz U.

ORTHODONTICS & CRANIOFACIAL RESEARCH, cilt.24, ss.117-123, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1111/ocr.12480
  • Dergi Adı: ORTHODONTICS & CRANIOFACIAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.117-123
  • Anahtar Kelimeler: artificial intelligence, cone&#8208, beam computed tomography, deep learning, pharyngeal airway, NEURAL-NETWORK, 3-DIMENSIONAL ANALYSIS, DIAGNOSIS, SURGERY, VOLUME
  • Ankara Üniversitesi Adresli: Evet

Özet

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.