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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.
Affiliation
  • 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 de En | MEDLINE | ID: mdl-37680621
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Diagnostic_studies / Guideline / Prognostic_studies Langue: En Journal: Front Med (Lausanne) Année: 2023 Type de document: Article Pays d'affiliation: Royaume-Uni Pays de publication: Suisse
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Diagnostic_studies / Guideline / Prognostic_studies Langue: En Journal: Front Med (Lausanne) Année: 2023 Type de document: Article Pays d'affiliation: Royaume-Uni Pays de publication: Suisse