Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wong:2022,
author = {Wong, N and Meshkinfamfard, S and Turbé, V and Whitaker, M and Moshe, M and Bardanzellu, A and Dai, T and Pignatelli, E and Barclay, W and Darzi, A and Elliott, P and Ward, H and Tanaka, R and Cooke, G and McKendry, R and Atchison, C and Bharath, A},
journal = {Communications Medicine},
title = {Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies},
url = {https://www.nature.com/articles/s43856-022-00146-z},
volume = {2},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Wong,N
AU  - Meshkinfamfard,S
AU  - Turbé,V
AU  - Whitaker,M
AU  - Moshe,M
AU  - Bardanzellu,A
AU  - Dai,T
AU  - Pignatelli,E
AU  - Barclay,W
AU  - Darzi,A
AU  - Elliott,P
AU  - Ward,H
AU  - Tanaka,R
AU  - Cooke,G
AU  - McKendry,R
AU  - Atchison,C
AU  - Bharath,A
PY  - 2022///
SN  - 2730-664X
TI  - Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies
T2  - Communications Medicine
UR  - https://www.nature.com/articles/s43856-022-00146-z
UR  - http://hdl.handle.net/10044/1/96363
VL  - 2
ER  -
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