Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern.
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| Title: | Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern. |
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| Authors: | Cho HN; Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea., Gwon E; Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea., Kim KA; Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea., Baek SH; Department of Orthodontics, School of Dentistry, Seoul National University, Seoul, South Korea., Kim N; Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Radiology, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. Electronic address: namkugkim@gmail.com., Kim SJ; Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea. Electronic address: ksj113@khu.ac.kr. |
| Source: | American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics [Am J Orthod Dentofacial Orthop] 2022 Aug; Vol. 162 (2), pp. e53-e62. Date of Electronic Publication: 2022 May 31. |
| Publication Type: | Journal Article; Randomized Controlled Trial |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 8610224 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-6752 (Electronic) Linking ISSN: 08895406 NLM ISO Abbreviation: Am J Orthod Dentofacial Orthop Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1097-6752 |
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| DOI: | 10.1016/j.ajodo.2022.01.011 |
