Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

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 mod...

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Bibliographic Details
Published in:	Nature communications Vol. 12; no. 1; pp. 634 - 11
Main Authors:	Lassau, Nathalie, Ammari, Samy, Chouzenoux, Emilie, Gortais, Hugo, Herent, Paul, Devilder, Matthieu, Soliman, Samer, Meyrignac, Olivier, Talabard, Marie-Pauline, Lamarque, Jean-Philippe, Dubois, Remy, Loiseau, Nicolas, Trichelair, Paul, Bendjebbar, Etienne, Garcia, Gabriel, Balleyguier, Corinne, Merad, Mansouria, Stoclin, Annabelle, Jegou, Simon, Griscelli, Franck, Tetelboum, Nicolas, Li, Yingping, Verma, Sagar, Terris, Matthieu, Dardouri, Tasnim, Gupta, Kavya, Neacsu, Ana, Chemouni, Frank, Sefta, Meriem, Jehanno, Paul, Bousaid, Imad, Boursin, Yannick, Planchet, Emmanuel, Azoulay, Mikael, Dachary, Jocelyn, Brulport, Fabien, Gonzalez, Adrian, Dehaene, Olivier, Schiratti, Jean-Baptiste, Schutte, Kathryn, Pesquet, Jean-Christophe, Talbot, Hugues, Pronier, Elodie, Wainrib, Gilles, Clozel, Thomas, Barlesi, Fabrice, Bellin, Marie-France, Blum, Michael G. B.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 27-01-2021 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 692/499 692/699/255/2514 Artificial Intelligence Computed tomography Coronaviruses COVID-19 COVID-19 - classification COVID-19 - diagnosis COVID-19 - physiopathology Deep Learning Engineering Sciences Hospitals Humanities and Social Sciences Humans Intensive care Intensive care units Models, Biological multidisciplinary Multivariate Analysis Network analysis Neural networks Neural Networks, Computer Oxygenation Pandemics Patients Prognosis Radiologists Science Science (multidisciplinary) Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Severity of Illness Index Signal and Image processing Tomography, X-Ray Computed - methods Urea Viral diseases
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Summary:	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. The SARS-COV-2 pandemic has put pressure on intensive care units, so that predicting severe deterioration early is a priority. Here, the authors develop a multimodal severity score including clinical and imaging features that has significantly improved prognostic performance in two validation datasets compared to previous scores.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-020-20657-4