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Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging.
Bridge, Joshua; Meng, Yanda; Zhu, Wenyue; Fitzmaurice, Thomas; McCann, Caroline; Addison, Cliff; Wang, Manhui; Merritt, Cristin; Franks, Stu; Mackey, Maria; Messenger, Steve; Sun, Renrong; Zhao, Yitian; Zheng, Yalin.
Afiliação
  • Bridge J; Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
  • Meng Y; Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
  • Zhu W; Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
  • Fitzmaurice T; Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
  • McCann C; Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom.
  • Addison C; Department of Radiology, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom.
  • Wang M; Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom.
  • Merritt C; Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom.
  • Franks S; Alces Flight Limited, Bicester, United Kingdom.
  • Mackey M; Alces Flight Limited, Bicester, United Kingdom.
  • Messenger S; Amazon Web Services, London, United Kingdom.
  • Sun R; Amazon Web Services, London, United Kingdom.
  • Zhao Y; Department of Radiology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Hubei University of Chinese Medicine, Wuhan, China.
  • Zheng Y; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
Front Med (Lausanne) ; 10: 1113030, 2023.
Article em En | MEDLINE | ID: mdl-37680621
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article