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dc.creatorAnastasopoulos, Constantin
dc.creatorWeikert, Thomas
dc.creatorYang, Shan
dc.creatorAbdulkadir, Ahmed
dc.creatorSchmülling, Lena
dc.creatorBühler, Claudia
dc.creatorPaciolla, Fabiano
dc.creatorSexauer, Raphael
dc.creatorCyriac, Joshy
dc.creatorNesic, Ivan
dc.creatorTwerenbold, Raphael
dc.creatorBremerich, Jens
dc.creatorStieltjes, Bram
dc.creatorSauter, Alexander W.
dc.creatorSommer, Gregor
dc.date.accessioned2020-09-28T15:13:18Z
dc.date.available2020-09-28T15:13:18Z
dc.date.created2020
dc.identifier.issn0720-048Xspa
dc.identifier.otherhttps://doi.org/10.1016/j.ejrad.2020.109233spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/13891
dc.description.abstractPurpose: During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic. Method: Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66). Results: The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up. Conclusions: The combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases.spa
dc.format.extent7 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherEuropean Journal of Radiologyspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectComputed tomographyspa
dc.subjectCOVID-19spa
dc.subjectMachine learningspa
dc.subjectSoftwarespa
dc.titleDevelopment and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learningspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1016/j.ejrad.2020.109233spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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