AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia

Chassagnon, Guillaume; Vakalopoulou, Maria; Battistella, Enzo; Christodoulidis, Stergios; Hoang-Thi, Trieu-Nghi; Dangeard, Severine; Deutsch, Eric; Andre, Fabrice; Guillo, Enora; Halm, Nara; Hajj, Stefany El; Bompard, Florian; Neveu, Sophie; Hani, Chahinez; Saab, Ines; Campredon, Aliénor; Koulakian, Hasmik; Bennani, Souhail; Freche, Gael; Barat, Maxime; Lombard, Aurelien; Fournier, Laure; Monnier, Hippolyte; Grand, Téodor; Gregory, Jules; Nguyen, Yann; Khalil, Antoine; Mahdjoub, Elyas; Brillet, Pierre-Yves; Tran Ba, Stéphane; Bousson, Valérie; Mekki, Ahmed; Carlier, Robert-Yves; Revel, Marie-Pierre; Paragios, Nikos

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.