AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2020

AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients

Emilie Chouzenoux
Paul Herent
  • Function : Author
Rémy Dubois
  • Function : Author
  • PersonId : 1000756
Nicolas Loiseau
  • Function : Author
Paul Trichelair
  • Function : Author
Etienne Bendjebbar
  • Function : Author
Simon Jegou
  • Function : Author
Sagar Verma
  • Function : Author
  • PersonId : 743250
  • IdHAL : versag
Frank Chemouni
  • Function : Author
  • PersonId : 918610
Meriem Sefta
  • Function : Author
Paul Jehanno
  • Function : Author
Jocelyn Dachary
  • Function : Author
Fabien Brulport
  • Function : Author
Adrián González
  • Function : Author
Olivier Dehaene
  • Function : Author
Jean-Baptiste Schiratti
Kathryn Schutte
  • Function : Author
Hugues Talbot
Elodie Pronier
  • Function : Author
Gilles Wainrib
  • Function : Author
Thomas Clozel
  • Function : Author
Fabrice Barlesi
Michael G B Blum
  • Function : Author
  • PersonId : 854172

Abstract

The SARS-COV-2 pandemic has put pressure on Intensive Care Units, and made theidentification of early predictors of disease severity a priority. We collected clinical,biological, chest CT scan data, and radiology reports from 1,003 coronavirus-infectedpatients from two French hospitals. Among 58 variables measured at admission, 11clinical and 3 radiological variables were associated with severity. Next, using 506,341chest CT images, we trained and evaluated deep learning models to segment thescans and reproduce radiologists' annotations. We also built CT image-based deeplearning models that predicted severity better than models based on the radiologists'reports. Finally, we showed that adding CT scan information—either throughradiologist lesion quantification or through deep learning—to clinical and biologicaldata, improves prediction of severity. These findings show that CT scans containnovel and unique prognostic information, which we included in a 6-variable ScanCovseverity score.
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Dates and versions

hal-02586111 , version 1 (15-05-2020)
hal-02586111 , version 2 (18-05-2020)
hal-02586111 , version 3 (03-07-2020)

Identifiers

  • HAL Id : hal-02586111 , version 3

Cite

Nathalie Lassau, Samy Ammari, Emilie Chouzenoux, Hugo Gortais, Paul Herent, et al.. AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients. [Research Report] Inria Saclay Ile de France. 2020. ⟨hal-02586111v3⟩
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