 Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patientsNathalie 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 ()
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 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20657-4 Ordering information: This journal article can be ordered from DOI: 10.1038/s41467-020-20657-4 Access Statistics for this article Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie More articles in Nature Communications from Nature |
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