Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients
Nathalie Lassau,
Samy Ammari,
Emilie Chouzenoux,
Hugo Gortais,
Paul Herent,
Matthieu Devilder,
Samer Soliman,
Olivier Meyrignac,
Marie-Pauline Talabard,
Jean-Philippe Lamarque,
Remy Dubois,
Nicolas Loiseau,
Paul Trichelair,
Etienne Bendjebbar,
Gabriel Garcia,
Corinne Balleyguier,
Mansouria Merad,
Annabelle Stoclin,
Simon Jegou,
Franck Griscelli,
Nicolas Tetelboum,
Yingping Li,
Sagar Verma,
Matthieu Terris,
Tasnim Dardouri,
Kavya Gupta,
Ana Neacsu,
Frank Chemouni,
Meriem Sefta,
Paul Jehanno,
Imad Bousaid,
Yannick Boursin,
Emmanuel Planchet,
Mikael Azoulay,
Jocelyn Dachary,
Fabien Brulport,
Adrian Gonzalez,
Olivier Dehaene,
Jean-Baptiste Schiratti,
Kathryn Schutte,
Jean-Christophe Pesquet,
Hugues Talbot,
Elodie Pronier,
Gilles Wainrib,
Thomas Clozel,
Fabrice Barlesi,
Marie-France Bellin and
Michael G. B. Blum ()
Additional contact information
Nathalie Lassau: Université Paris -Saclay
Samy Ammari: Université Paris -Saclay
Emilie Chouzenoux: Université Paris-Saclay, CentraleSupélec, Inria
Hugo Gortais: Université Paris-Saclay
Paul Herent: Owkin, Inc
Matthieu Devilder: Université Paris-Saclay
Samer Soliman: Université Paris-Saclay
Olivier Meyrignac: Université Paris-Saclay
Marie-Pauline Talabard: Université Paris-Saclay
Jean-Philippe Lamarque: Université Paris -Saclay
Remy Dubois: Owkin, Inc
Nicolas Loiseau: Owkin, Inc
Paul Trichelair: Owkin, Inc
Etienne Bendjebbar: Owkin, Inc
Gabriel Garcia: Université Paris -Saclay
Corinne Balleyguier: Université Paris -Saclay
Mansouria Merad: Université Paris-Saclay
Annabelle Stoclin: Université Paris-Saclay
Simon Jegou: Owkin, Inc
Franck Griscelli: Université Paris-Saclay
Nicolas Tetelboum: Université Paris -Saclay
Yingping Li: Université Paris-Saclay
Sagar Verma: Université Paris-Saclay, CentraleSupélec, Inria
Matthieu Terris: Université Paris-Saclay, CentraleSupélec, Inria
Tasnim Dardouri: Université Paris-Saclay, CentraleSupélec, Inria
Kavya Gupta: Université Paris-Saclay, CentraleSupélec, Inria
Ana Neacsu: Université Paris-Saclay, CentraleSupélec, Inria
Frank Chemouni: Université Paris-Saclay
Meriem Sefta: Owkin, Inc
Paul Jehanno: Owkin, Inc
Imad Bousaid: Université Paris-Saclay
Yannick Boursin: Université Paris-Saclay
Emmanuel Planchet: Université Paris-Saclay
Mikael Azoulay: Université Paris-Saclay
Jocelyn Dachary: Owkin, Inc
Fabien Brulport: Owkin, Inc
Adrian Gonzalez: Owkin, Inc
Olivier Dehaene: Owkin, Inc
Jean-Baptiste Schiratti: Owkin, Inc
Kathryn Schutte: Owkin, Inc
Jean-Christophe Pesquet: Université Paris-Saclay, CentraleSupélec, Inria
Hugues Talbot: Université Paris-Saclay, CentraleSupélec, Inria
Elodie Pronier: Owkin, Inc
Gilles Wainrib: Owkin, Inc
Thomas Clozel: Owkin, Inc
Fabrice Barlesi: Université Paris-Saclay
Marie-France Bellin: Université Paris-Saclay
Michael G. B. Blum: Owkin, Inc
Nature Communications, 2021, vol. 12, issue 1, 1-11
Abstract:
Abstract The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20657-4
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DOI: 10.1038/s41467-020-20657-4
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