About: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)
Creator	Yang, Yang Shan, Hong Bernheim, Adam Chung, Michael Huang, Mingqian Jacobi, Adam Li, Kunwei Li, Shaolin Lin, Bin Mei, Xueyan Lv, Jian Calcagno, Claudia Fayad, Zahi Little, Brent Steinberger, Sharon Xie, Zongyu Luksza, Marta Deyer, Timothy Kaiyue-Diao, Lee, Hao-Chih Liu, Chenyu Long, Qihua Ma, Yixuan Mani, Venkatesh Robson, Philip Xia, Junli Zhao, Tongtong
Source	MedRxiv; Medline
abstract	The coronavirus disease 2019 (COVID-19) outbreak that originated in Wuhan, China has rapidly propagated due to widespread person-to-person transmission and has resulted in over 1,133,758 cases in 197 countries with a total of 62,784 deaths as of April 5, 2020. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test. This test can take up to two days to complete, and, due to the possibility of false negatives, serial testing may be required to reliably exclude infection. A current supply shortage of RT-PCR test kits compounds the shortcomings of entrusting diagnosis to the PCR test alone and underscores the urgent need to provide alternative methods for the rapid and accurate diagnosis of SARS-CoV-2 patients. 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 to fully exclude infection, because of the normal radiologic findings in some early disease patients. In this study, we use artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and/or laboratory testing to more accurately and rapidly diagnose SARS-CoV-2 (+) patients. We included 905 RT-PCR confirmed patients. 419 (46.2%) tested positive for SARS-CoV-2 by laboratory-confirmed real-time RT-PCR assay and next-generation sequencing, while 486 patients (53.8%) tested negative (confirmed by at least two additional negative RT-PCR tests and clinical observation). The proposed AI system achieved an AUC of 0.92 and performed equally well in sensitivity compared to a senior thoracic radiologist on a testing set of 279 cases. The AI system also improved the detection of RT-PCR positive SARS-CoV-2 patients who presented with normal CTs, correctly identifying 17/25 (68%) patients, whereas all 25 RT-PCR SARS-CoV-2-positive CT-normal patients were classified as SARS-CoV-2 negative by radiologists.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1101/2020.04.12.20062661
bibo:pmid	32511559
has license	medrxiv
sha1sum (hex)	04e5751f0a3333da7b6053e3e9336c7710b229ca
schema:url	https://doi.org/10.1101/2020.04.12.20062661
resource representing a document's title	Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)
has PubMed identifier	32511559
schema:publication	medRxiv : the preprint server for health sciences
resource representing a document's body	covid:04e5751f0a3333da7b6053e3e9336c7710b229ca#body_text
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