Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

Kang, Seyun; Kim, Inhwan; Kim, Yoon-Ji; Kim, Namkug; Baek, Seung-Hak; Sung, Sang-Jin

Kang, Seyun; Kim, Inhwan; Kim, Yoon-Ji; Kim, Namkug; Baek, Seung-Hak; Sung, Sang-Jin.

Afiliación

Kang S; Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Kim I; Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Kim YJ; Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 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, Korea.
Baek SH; Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea.
Sung SJ; Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Orthod Craniofac Res ; 27(1): 64-77, 2024 Feb.

Article en En | MEDLINE | ID: mdl-37326233

Asunto(s)

Cara; Redes Neurales de la Computación; Humanos; Adulto Joven; Adulto; Cefalometría; Radiografía; Reproducibilidad de los Resultados

Palabras clave

artificial intelligence; cephalometry; deep learning; diagnosis; orthodontics

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Cara Tipo de estudio: Prognostic_studies Límite: Adult / Humans Idioma: En Revista: Orthod Craniofac Res Asunto de la revista: ODONTOLOGIA / ORTODONTIA Año: 2024 Tipo del documento: Article

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