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dc.creatorChassagnon, Guillaume
dc.creatorVakalopoulou, Maria
dc.creatorBattistella, Enzo
dc.creatorChristodoulidis, Stergios
dc.creatorHoang-Thi, Trieu-Nghi
dc.creatorDangeard, Severine
dc.creatorDeutsch, Eric
dc.creatorAndre, Fabrice
dc.creatorGuillo, Enora
dc.creatorHalm, Nara
dc.creatorHajj, Stefany El
dc.creatorBompard, Florian
dc.creatorNeveu, Sophie
dc.creatorHani, Chahinez
dc.creatorSaab, Ines
dc.creatorCampredon, Alienor
dc.creatorKoulakian, Hasmik
dc.creatorBennani, Souhail
dc.creatorFreche, Gael
dc.creatorBarat, Maxime
dc.creatorLombard, Aurelien
dc.creatorFournier, Laure
dc.creatorMonnier, Hippolyte
dc.creatorGrand, Teodor
dc.creatorGregory, Jules
dc.creatorNguyen, Yann
dc.creatorKhalil, Antoine
dc.creatorMahdjoub, Elyas
dc.creatorBrillet, Pierre-Yves
dc.creatorTran Ba, Stephane
dc.creatorBousson, Valérie
dc.creatorMekki, Ahmed
dc.creatorCarlier, Robert-Yves
dc.creatorRevel, Marie-Pierre
dc.creatorParagios, Nikos
dc.description.abstractCoronavirus 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
dc.format.extent25 páginasspa
dc.publisherMedical Image Analysisspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCOVID 19 pneumoniaspa
dc.subjectArtifial Intelligencespa
dc.subjectDeep Learningspa
dc.subjectBiomarker discoveryspa
dc.subjectEnsemble methodsspa
dc.titleAI-Driven quantification, staging and outcome prediction of COVID-19 pneumoniaspa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.rights.localAbierto (Texto Completo)spa

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