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Trieu-Nghi Hoang-Thi

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

Apr 20, 2020
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard, Sophie Neveu, Chahinez Hani, Ines Saab, Alienor Campredon, Hasmik Koulakian, Souhail Bennani, Gael Freche, Aurelien Lombard, Laure Fournier, Hippolyte Monnier, Teodor Grand, Jules Gregory, Antoine Khalil, Elyas Mahdjoub, Pierre-Yves Brillet, Stephane Tran Ba, Valerie Bousson, Marie-Pierre Revel, Nikos Paragios

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.

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