  Chest computed tomography (CT) is widely used for the management of
Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and
rapidity. The standard of reference for confirming COVID-19 relies on
microbiological tests but these tests might not be available in an emergency
setting and their results are not immediately available, contrary to CT. In
addition to its role for early diagnosis, CT has a prognostic role by allowing
visually evaluating the extent of COVID-19 lung abnormalities. The objective of
this study is to address prediction of short-term outcomes, especially need for
mechanical ventilation. In this multi-centric study, we propose an end-to-end
artificial intelligence solution for automatic quantification and prognosis
assessment by combining automatic CT delineation of lung disease meeting
performance of experts and data-driven identification of biomarkers for its
prognosis. AI-driven combination of variables with CT-based biomarkers offers
perspectives for optimal patient management given the shortage of intensive
care beds and ventilators.
