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Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.
Nagaraj, Yeshaswini; de Jonge, Gonda; Andreychenko, Anna; Presti, Gabriele; Fink, Matthias A; Pavlov, Nikolay; Quattrocchi, Carlo C; Morozov, Sergey; Veldhuis, Raymond; Oudkerk, Matthijs; van Ooijen, Peter M A.
Afiliación
  • Nagaraj Y; Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. y.nagaraj@umcg.nl.
  • de Jonge G; Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. y.nagaraj@umcg.nl.
  • Andreychenko A; Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Presti G; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia.
  • Fink MA; Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
  • Pavlov N; Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
  • Quattrocchi CC; Translational Lung Research Center Heidelberg, member of the German Center for Lung Research, Heidelberg, Germany.
  • Morozov S; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia.
  • Veldhuis R; Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
  • Oudkerk M; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia.
  • van Ooijen PMA; Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data management Biometrics (DMB), University of Twente, Enschede, The Netherlands.
Eur Radiol ; 32(9): 6384-6396, 2022 Sep.
Article en En | MEDLINE | ID: mdl-35362751
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neumonía / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Aged80 / Humans / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Países Bajos
Texto completo
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neumonía / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Aged80 / Humans / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Países Bajos