Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks.

Shin, Nan-Young; Lee, Byoung-Dai; Kang, Ju-Hee; Kim, Hye-Rin; Oh, Dong Hyo; Lee, Byung Il; Kim, Sung Hyun; Lee, Mu Sook; Heo, Min-Suk

Shin, Nan-Young; Lee, Byoung-Dai; Kang, Ju-Hee; Kim, Hye-Rin; Oh, Dong Hyo; Lee, Byung Il; Kim, Sung Hyun; Lee, Mu Sook; Heo, Min-Suk.

Afiliación

Shin NY; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea.
Lee BD; Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Seoul, Korea.
Kang JH; Division of Computer Science and Engineering, Kyonggi University, Suwon, Korea.
Kim HR; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea.
Oh DH; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea.
Lee BI; Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Seoul, Korea.
Kim SH; Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Seoul, Korea.
Lee MS; Center for Artificial Intelligence in Medicine and Imaging, HealthHub, Seoul, Korea.
Heo MS; Department of Radiology, Keimyung University, Dongsan Hospital, Daegu, Korea.

Imaging Sci Dent ; 50(3): 237-243, 2020 Sep.

Article en En | MEDLINE | ID: mdl-33005581

Palabras clave

Age Determination by Skeleton; Artificial Intelligence; Deep Learning; Radiography

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Imaging Sci Dent Año: 2020 Tipo del documento: Article Pais de publicación: