About: AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients

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About: AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients
Creator	Chemouni, Frank Stoclin, Annabelle Balleyguier, Corinne Barlesi, Fabrice Griscelli, Franck Merad, Mansouria Ammari, Samy Azoulay, Mikael Bellin, Marie-France Bendjebbar, Etienne Blum, Michael Boursin, Yannick Bousaid, Imad Brulport, Fabien Chouzenoux, Emilie Clozel, Thomas Dachary, Jocelyn Dardouri, Tasnim Dehaene, Olivier Devilder, Matthieu Dubois, Remy Garcia, Gabriel Gonzalez, Adrian Gortais, Hugo Gupta, Kavya Herent, Paul Jegou, Simon Jehanno, Paul Lamarque, Jean-Philippe Lassau, Nathalie Li, Yingping Loiseau, Nicolas Meyrignac, Olivier Neacsu, Ana Pesquet, Jean-Christophe Planchet, Emmanuel Pronier, Elodie Schiratti, Jean-Baptiste Schutte, Kathryn Sefta, Meriem Soliman, Samer Talabard, Marie-Pauline Talbot, Hugues Terris, Matthieu Tetelboum, Nicolas Trichelair, Paul Verma, Sagar Wainrib, Gilles
Source	MedRxiv
abstract	With 15% of severe cases among hospitalized patients1, the SARS-COV-2 pandemic has put tremendous pressure on Intensive Care Units, and made the identification of early predictors of severity a public health priority. We collected clinical and biological data, as well as CT scan images and radiology reports from 1,003 coronavirus-infected patients from two French hospitals. Radiologists' manual CT annotations were also available. We first identified 11 clinical variables and 3 types of radiologist-reported features significantly associated with prognosis. Next, focusing on the CT images, we trained deep learning models to automatically segment the scans and reproduce radiologists' annotations. We also built CT image-based deep learning models that predicted severity better than models based on the radiologists' scan reports. Finally, we showed that including CT scan features alongside the clinical and biological data yielded more accurate predictions than using clinical and biological data only. These findings show that CT scans provide insightful early predictors of severity.
has issue date	2020-05-19(xsd:dateTime)
bibo:doi	10.1101/2020.05.14.20101972
has license	medrxiv
sha1sum (hex)	005aa3503919dcc7f016ff60c0cf86417e26ed8f
schema:url	https://doi.org/10.1101/2020.05.14.20101972
resource representing a document's title	AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients
resource representing a document's body	covid:005aa3503919dcc7f016ff60c0cf86417e26ed8f#body_text
is schema:about of	named entity 'segment' named entity 'based' named entity 'radiology' named entity 'patients' named entity 'These' named entity 'models' named entity 'tests' named entity 'OUTCOME PREDICTION' named entity 'CLINICAL CHARACTERISTICS' named entity 'HOSPITALIZED PATIENTS' named entity 'cases' named entity 'Units' named entity 'public health' named entity 'hospitals' named entity 'images' named entity 'hospitalized' named entity 'pandemic' named entity 'future' named entity 'variables' named entity 'future' named entity 'CT scans' named entity 'models' named entity 'radiologists' named entity 'CTs' named entity 'preprint' named entity 'radiologists' named entity 'prognosis' named entity 'radiology' named entity 'CT scans' named entity 'coronavirus' named entity 'SARS-COV-2' named entity 'CT scan' named entity 'deep learning' named entity 'clinical characteristics' named entity 'feature space' named entity 'lesion' named entity 'Kremlin-Bicêtre' named entity 'validation set'

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