Predicting hip-knee-ankle and femorotibial angles from knee radiographs with deep learning.

Wang, Jinhong; Hall, Thomas A G; Musbahi, Omar; Jones, Gareth G; van Arkel, Richard J

Wang, Jinhong; Hall, Thomas A G; Musbahi, Omar; Jones, Gareth G; van Arkel, Richard J.

Afiliação

Wang J; Department of Mechanical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Hall TAG; Department of Mechanical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Musbahi O; Department of Surgery and Cancer, Imperial College London, White City Campus, London W12 0BZ, United Kingdom.
Jones GG; Department of Surgery and Cancer, Imperial College London, White City Campus, London W12 0BZ, United Kingdom.
van Arkel RJ; Department of Mechanical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom. Electronic address: r.vanarkel@imperial.ac.uk.

Knee ; 42: 281-288, 2023 Jun.

Article em En | MEDLINE | ID: mdl-37119601

Assuntos

Aprendizado Profundo; Osteoartrite do Joelho; Humanos; Tornozelo; Reprodutibilidade dos Testes; Articulação do Joelho/cirurgia; Extremidade Inferior; Osteoartrite do Joelho/cirurgia; Estudos Retrospectivos

Palavras-chave

Artificial Intelligence; Knee Angle; Mechanical Alignment; Neural Network; Surgical planning; X-ray

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoartrite do Joelho / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Knee Assunto da revista: ORTOPEDIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Holanda

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