AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia
Autor
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, Alienor
Koulakian, Hasmik
Bennani, Souhail
Freche, Gael
Barat, Maxime
Lombard, Aurelien
Fournier, Laure
Monnier, Hippolyte
Grand, Teodor
Gregory, Jules
Nguyen, Yann
Khalil, Antoine
Mahdjoub, Elyas
Brillet, Pierre-Yves
Tran Ba, Stephane
Bousson, Valérie
Mekki, Ahmed
Carlier, Robert-Yves
Revel, Marie-Pierre
Paragios, Nikos
Institución
Resumen
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 datadriven 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.