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

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Title: Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies
Authors: Wong, N
Meshkinfamfard, S
Turbé, V
Whitaker, M
Moshe, M
Bardanzellu, A
Dai, T
Pignatelli, E
Barclay, W
Darzi, A
Elliott, P
Ward, H
Tanaka, R
Cooke, G
McKendry, R
Atchison, C
Bharath, A
Item Type: Journal Article
Abstract: Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home but rely on subjective interpretation of a test line by eye, risking false positives and negatives. Here we report the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Automated analysis showed substantial agreement with human experts (Kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false positive and false negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests, to be a tool for improved accuracy for population-level community surveillance.
Issue Date: 6-Jul-2022
Date of Acceptance: 11-Apr-2022
URI: http://hdl.handle.net/10044/1/96363
ISSN: 2730-664X
Publisher: Nature Research
Journal / Book Title: Communications Medicine
Volume: 2
Copyright Statement: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2022
Sponsor/Funder: Department of Health
Department of Health
Engineering & Physical Science Research Council (E
Funder's Grant Number: n/a
n/a
EP/R511547/1
Keywords: Databases
Public health
Publication Status: Published
Article Number: ARTN 78
Appears in Collections:Bioengineering
Department of Surgery and Cancer
Department of Infectious Diseases
Faculty of Medicine
Institute of Global Health Innovation
School of Public Health
Faculty of Engineering