dc.creator | Chassagnon, Guillaume | |
dc.creator | Vakalopoulou, Maria | |
dc.creator | Battistella, Enzo | |
dc.creator | Christodoulidis, Stergios | |
dc.creator | Hoang-Thi, Trieu-Nghi | |
dc.creator | Dangeard, Severine | |
dc.creator | Deutsch, Eric | |
dc.creator | Andre, Fabrice | |
dc.creator | Guillo, Enora | |
dc.creator | Halm, Nara | |
dc.creator | Hajj, Stefany El | |
dc.creator | Bompard, Florian | |
dc.creator | Neveu, Sophie | |
dc.creator | Hani, Chahinez | |
dc.creator | Saab, Ines | |
dc.creator | Campredon, Alienor | |
dc.creator | Koulakian, Hasmik | |
dc.creator | Bennani, Souhail | |
dc.creator | Freche, Gael | |
dc.creator | Barat, Maxime | |
dc.creator | Lombard, Aurelien | |
dc.creator | Fournier, Laure | |
dc.creator | Monnier, Hippolyte | |
dc.creator | Grand, Teodor | |
dc.creator | Gregory, Jules | |
dc.creator | Nguyen, Yann | |
dc.creator | Khalil, Antoine | |
dc.creator | Mahdjoub, Elyas | |
dc.creator | Brillet, Pierre-Yves | |
dc.creator | Tran Ba, Stephane | |
dc.creator | Bousson, Valérie | |
dc.creator | Mekki, Ahmed | |
dc.creator | Carlier, Robert-Yves | |
dc.creator | Revel, Marie-Pierre | |
dc.creator | Paragios, Nikos | |
dc.date.accessioned | 2020-10-19T20:27:42Z | |
dc.date.available | 2020-10-19T20:27:42Z | |
dc.date.created | 2020 | |
dc.identifier.issn | 1361-8415 | spa |
dc.identifier.other | https://doi.org/10.1016/j.media.2020.101860 | spa |
dc.identifier.uri | http://hdl.handle.net/20.500.12010/14595 | |
dc.description.abstract | 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. | spa |
dc.format.extent | 25 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Medical Image Analysis | spa |
dc.source | reponame:Expeditio Repositorio Institucional UJTL | spa |
dc.source | instname:Universidad de Bogotá Jorge Tadeo Lozano | spa |
dc.subject | COVID 19 pneumonia | spa |
dc.subject | Artifial Intelligence | spa |
dc.subject | Deep Learning | spa |
dc.subject | Staging | spa |
dc.subject | Prognosis | spa |
dc.subject | Biomarker discovery | spa |
dc.subject | Ensemble methods | spa |
dc.title | AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia | spa |
dc.type.local | Artículo | spa |
dc.subject.lemb | Síndrome respiratorio agudo grave | spa |
dc.subject.lemb | COVID-19 | spa |
dc.subject.lemb | SARS-CoV-2 | spa |
dc.subject.lemb | Coronavirus | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | spa |
dc.rights.local | Abierto (Texto Completo) | spa |
dc.identifier.doi | https://doi.org/10.1016/j.media.2020.101860 | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |