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Chest computed tomography (CT) is widely used for the management ofCoronavirus disease 2019 (COVID-19) pneumonia because of its availability andrapidity. The standard of reference for confirming COVID-19 relies onmicrobiological tests but these tests might not be available in an emergencysetting and their results are not immediately available, contrary to CT. Inaddition to its role for early diagnosis, CT has a prognostic role by allowingvisually evaluating the extent of COVID-19 lung abnormalities. The objective ofthis study is to address prediction of short-term outcomes, especially need formechanical ventilation. In this multi-centric study, we propose an end-to-endartificial intelligence solution for automatic quantification and prognosisassessment by combining automatic CT delineation of lung disease meetingperformance of experts and data-driven identification of biomarkers for itsprognosis. AI-driven combination of variables with CT-based biomarkers offersperspectives for optimal patient management given the shortage of intensivecare beds and ventilators.

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia