Communications Medicine (Jul 2022)

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies

  • Nathan C. K. Wong,
  • Sepehr Meshkinfamfard,
  • Valérian Turbé,
  • Matthew Whitaker,
  • Maya Moshe,
  • Alessia Bardanzellu,
  • Tianhong Dai,
  • Eduardo Pignatelli,
  • Wendy Barclay,
  • Ara Darzi,
  • Paul Elliott,
  • Helen Ward,
  • Reiko J. Tanaka,
  • Graham S. Cooke,
  • Rachel A. McKendry,
  • Christina J. Atchison,
  • Anil A. Bharath

DOI
https://doi.org/10.1038/s43856-022-00146-z
Journal volume & issue
Vol. 2, no. 1
pp. 1 – 10

Abstract

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Wong et al. describe a machine learning approach for visual auditing of lateral flow tests for SARS-CoV-2 antibodies. Their automated analysis shows strong agreement with experts and consistently better performance than non-expert study participants at classifying positive results.