Artificial intelligence–enabled rapid diagnosis of patients with COVID-19 (Registro nro. 5980)

000 -LEADER
fixed length control field 02563nam a22003017c 4500
001 - CONTROL NUMBER
control field 00005980
003 - CONTROL NUMBER IDENTIFIER
control field ES-MaONT
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20211006062659.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200525t2020 |||||o|||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1078-8956
040 ## - CATALOGING SOURCE
Original cataloging agency ES-MaONT
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Mei, Xueyan
9 (RLIN) 4475
245 00 - TITLE STATEMENT
Title Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Nature Medicine,
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent 14 págs.
336 ## - CONTENT TYPE
Source isbdcontent
Content type term texto (visual)
337 ## - MEDIA TYPE
Source isbdmedia
Media type term electrónico
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term recurso en línea
520 ## - SUMMARY, ETC.
Summary, etc. For 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.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Sanidad digital
Source of heading or term
9 (RLIN) 2065
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4348
Topical term or geographic name entry element Inteligencia Artificial
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term coronavirus
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term COVID-19
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term deep learning
856 4# - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://www.nature.com/articles/s41591-020-0931-3
Nonpublic note Abierto
Link text Acceso al artículo
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Artículos
Existencias
Withdrawn status Lost status Materials specified (bound volume or other part) Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type Public note
    Artículo   Acceso libre online Colección digital CDO CDO   25/05/2020   1000020176183 25/05/2020 25/05/2020 Artículos .pdf, .html
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