Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks

Min Jing, Donal McLaughlin, David Steele, Sara McNamee, Brian MacNamee, Patrick Cullen, Dewar Finlay, James McLaughlin

2020

Abstract

Lateral Flow Immunoassays (LFA) have the potential to provide low cost, rapid and highly efficacious Point-of-Care (PoC) diagnostic testing in resource limited settings. Traditional LFA testing is semi-quantitative based on the calibration curve, which faces challenges in the detection of multilevel high-sensitivity biomarkers due its low sensitivity. This paper proposes a novel framework in which the LFA images are acquired from a designed CMOS reader system under controlled lighting. Unlike most existing approaches based on image intensity, the proposed system does not require detection of region of interest (ROI), instead each row of the LFA image was considered as time series signals. The Long Short-Term Memory (LSTM) network was deployed to classify the LFA data obtained from cardiovascular biomarker, C-Reactive Protein (CRP), at eight concentration levels (within the range 0-5mg/L) that are aligned with clinically actionable categories. The performance under different arrangements for input dimension and parameters were evaluated. The preliminary results show that the proposed LSTM outperforms other popular classification methods, which demonstrate the capability of the proposed system to detect high-sensitivity CRP and suggests the potential of applications for early risk assessment of cardiovascular diseases (CVD).

Paper Citation

in Harvard Style

Jing M., McLaughlin D., Steele D., McNamee S., MacNamee B., Cullen P., Finlay D. and McLaughlin J. (2020). Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING; ISBN 978-989-758-398-8, SciTePress, pages 177-183. DOI: 10.5220/0009117901770183

in Bibtex Style

@conference{bioimaging20,
author={Min Jing and Donal McLaughlin and David Steele and Sara McNamee and Brian MacNamee and Patrick Cullen and Dewar Finlay and James McLaughlin},
title={Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING},
year={2020},
pages={177-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009117901770183},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING
TI - Detection and Categorisation of Multilevel High-sensitivity Cardiovascular Biomarkers from Lateral Flow Immunoassay Images via Recurrent Neural Networks
SN - 978-989-758-398-8
AU - Jing M.
AU - McLaughlin D.
AU - Steele D.
AU - McNamee S.
AU - MacNamee B.
AU - Cullen P.
AU - Finlay D.
AU - McLaughlin J.
PY - 2020
SP - 177
EP - 183
DO - 10.5220/0009117901770183
PB - SciTePress