Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

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dc.creatorMei, Xueyan
dc.creatorLee, Hao Chih
dc.creatorDiao, Kai yue
dc.creatorHuang, Mingqian
dc.creatorLin, Bin
dc.creatorLiu, Chenyu
dc.creatorXie, Zongyu
dc.creatorMa, Yixuan
dc.creatorRobson, Philip M.
dc.creatorChung, Michael
dc.creatorBernheim, Adam
dc.creatorMani, Venkatesh
dc.creatorCalcagno, Claudia
dc.creatorLi, Kunwei
dc.creatorLi, Shaolin
dc.creatorShan, Hong
dc.creatorLv, Jian
dc.creatorZhao, Tongtong
dc.creatorXia, Junli
dc.creatorLong, Qihua
dc.creatorSteinberger, Sharon
dc.creatorJacobi, Adam
dc.creatorDeyer, Timothy
dc.creatorLuksza, Marta
dc.creatorLiu, Fang
dc.creatorLittle, Brent P.
dc.creatorFayad, Zahi A.
dc.creatorYang, Yang
dc.date.accessioned2020-07-17T19:52:50Z
dc.date.available2020-07-17T19:52:50Z
dc.date.created2020-05-19
dc.description.abstractenglishFor diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT–PCR assay and next-generation sequencing RT–PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.spa
dc.format.extent14 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1038/s41591-020-0931-3spa
dc.identifier.issn1546-170Xspa
dc.identifier.otherhttps://www.nature.com/articles/s41591-020-0931-3#article-infospa
dc.identifier.urihttps://hdl.handle.net/20.500.12010/10799
dc.publisherScience Directeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectInteligencia artificialspa
dc.subject.keywordArtificial intelligencespa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.titleArtificial intelligence–enabled rapid diagnosis of patients with COVID-19spa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localArtículospa

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