Back to Search Start Over

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

Authors :
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
Source :
Communications Medicine, Vol 2, Iss 1, Pp 1-10 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

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.

Subjects

Subjects :
Medicine

Details

Language :
English
ISSN :
2730664X
Volume :
2
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Communications Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.07b76ff56a8546e2bdfba18d8ae2889a
Document Type :
article
Full Text :
https://doi.org/10.1038/s43856-022-00146-z