AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Medical Image Analysis Year : 2021

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

Trieu-Nghi Hoang-Thi
Laure Fournier
Jules Gregory
Yann Nguyen
Antoine Khalil


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.
Fichier principal
Vignette du fichier
S1361841520302243.pdf (4.09 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03133234 , version 1 (21-11-2022)




Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, et al.. AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis, 2021, 67, pp.101860. ⟨10.1016/⟩. ⟨hal-03133234⟩
228 View
65 Download



Gmail Mastodon Facebook X LinkedIn More