Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view.

Cho, H-Y; Lee, K; Kong, H-J; Yang, H-L; Jung, C-W; Park, H-P; Hwang, J Y; Lee, H-C

Cho, H-Y; Lee, K; Kong, H-J; Yang, H-L; Jung, C-W; Park, H-P; Hwang, J Y; Lee, H-C.

Afiliação

Cho HY; Department of Anaesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Lee K; Department of Anaesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Kong HJ; Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea.
Yang HL; Medical Big data Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Jung CW; Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
Park HP; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
Hwang JY; Department of Anaesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Lee HC; Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.

Anaesthesia ; 78(1): 64-72, 2023 01.

Article em En | MEDLINE | ID: mdl-36198200

Assuntos

Aprendizado Profundo; Humanos

Palavras-chave

airway evaluation; artificial intelligence; deep-learning; difficult laryngoscopy; intratracheal; intubation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Anaesthesia Ano de publicação: 2023 Tipo de documento: Article

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