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In Nature communications ; h5-index 260.0

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.

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