Article (Scientific journals)
Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity
Guiot, Julien; Maes, Nathalie; WINANDY, Marie-Laurence et al.
2022In Frontiers in Medicine, 9
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Keywords :
General Medicine; SARS-CoV-2; Artificial Intelligence; CT scan analysis
Abstract :
[en] The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.
Disciplines :
Radiology, nuclear medicine & imaging
Cardiovascular & respiratory systems
Author, co-author :
Guiot, Julien  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
Maes, Nathalie ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des informations médico économiques (SIME)
WINANDY, Marie-Laurence  ;  Centre Hospitalier Universitaire de Liège - CHU > > Centre d'oncologie
Henket, Monique ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
Ernst, Benoit  ;  Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
THYS, Marie ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des informations médico économiques (SIME)
Frix, Anne-Noëlle ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
Morimont, Philippe ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
Rousseau, Anne-Françoise  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
CANIVET, Perrine ;  Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiodiagnostic
Louis, Renaud ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
Misset, Benoît ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
Meunier, Paul ;  Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiodiagnostic
Charbonnier, Jean-Paul
Lambermont, Bernard  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
More authors (5 more) Less
Language :
English
Title :
Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity
Publication date :
29 August 2022
Journal title :
Frontiers in Medicine
eISSN :
2296-858X
Publisher :
Frontiers Media SA
Volume :
9
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 101005122 - DRAGON - The RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics
Funders :
EU - European Union [BE]
IMI - Innovative Medicines Initiative [BE]
Available on ORBi :
since 12 September 2022

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