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Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.
Richardson, Shannon; Kohn, Michael A; Bollyky, Jenna; Parsonnet, Julie.
  • Richardson S; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
  • Kohn MA; Department of Emergency Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Bollyky J; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
  • Parsonnet J; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: parsonnt@stanford.edu.
Diagn Microbiol Infect Dis ; 104(3): 115763, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1914300
ABSTRACT

BACKGROUND:

The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield results whereas lateral flow tests can lack sensitivity.

METHODS:

We tested a rapid, lateral flow antigen (LFA) assay with artificial intelligence read (LFAIR) in subjects from COVID-19 treatment trials (N = 37; daily tests for 5 days) and from a population-based study (N = 88; single test). LFAIR was compared to RT-PCR from same-day samples.

RESULTS:

Using each participant's first sample, LFAIR showed 86.2% sensitivity (95% CI 73.6%-98.8) and 94.3% specificity (88.8%-99.7%) compared to RT-PCR. Adjusting for days since symptom onset and repeat testing, sensitivity was 97.8% (89.9%-99.5%) on the first symptomatic day and decreased with each additional day. Sensitivity improved with artificial intelligence (AI) read (86.2%) compared to the human eye (71.4%).

CONCLUSION:

LFAIR showed improved accuracy compared to LFA alone. particularly early in infection.
Subject(s)
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / COVID-19 Serological Testing / SARS-CoV-2 / COVID-19 / Antigens, Viral Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Diagn Microbiol Infect Dis Year: 2022 Document Type: Article Affiliation country: J.diagmicrobio.2022.115763

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / COVID-19 Serological Testing / SARS-CoV-2 / COVID-19 / Antigens, Viral Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Diagn Microbiol Infect Dis Year: 2022 Document Type: Article Affiliation country: J.diagmicrobio.2022.115763