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Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows

Despite their simplicity, lateral flow immunoassays (LFIAs) remain a crucial weapon in the diagnostic arsenal, particularly at the point-of-need. However, methods for analysing LFIAs still rely heavily on sub-optimal human readout and rudimentary end-point analysis. This negatively impacts both test...

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Autores principales: Colombo, Monika, Bezinge, Léonard, Rocha Tapia, Andres, Shih, Chih-Jen, de Mello, Andrew J., Richards, Daniel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850356/
https://www.ncbi.nlm.nih.gov/pubmed/36741250
http://dx.doi.org/10.1039/d2sd00197g
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author Colombo, Monika
Bezinge, Léonard
Rocha Tapia, Andres
Shih, Chih-Jen
de Mello, Andrew J.
Richards, Daniel A.
author_facet Colombo, Monika
Bezinge, Léonard
Rocha Tapia, Andres
Shih, Chih-Jen
de Mello, Andrew J.
Richards, Daniel A.
author_sort Colombo, Monika
collection PubMed
description Despite their simplicity, lateral flow immunoassays (LFIAs) remain a crucial weapon in the diagnostic arsenal, particularly at the point-of-need. However, methods for analysing LFIAs still rely heavily on sub-optimal human readout and rudimentary end-point analysis. This negatively impacts both testing accuracy and testing times, ultimately lowering diagnostic throughput. Herein, we present an automated computational imaging method for processing and analysing multiple LFIAs in real-time and in parallel. This method relies on the automated detection of signal intensity at the test line, control line, and background, and employs statistical comparison of these values to predictively categorise tests as “positive”, “negative”, or “failed”. We show that such a computational methodology can be transferred to a smartphone and detail how real-time analysis of LFIAs can be leveraged to decrease the time-to-result and increase testing throughput. We compare our method to naked-eye readout and demonstrate a shorter time-to-result across a range of target antigen concentrations and fewer false negatives compared to human subjects at low antigen concentrations.
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spelling pubmed-98503562023-02-03 Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows Colombo, Monika Bezinge, Léonard Rocha Tapia, Andres Shih, Chih-Jen de Mello, Andrew J. Richards, Daniel A. Sens Diagn Chemistry Despite their simplicity, lateral flow immunoassays (LFIAs) remain a crucial weapon in the diagnostic arsenal, particularly at the point-of-need. However, methods for analysing LFIAs still rely heavily on sub-optimal human readout and rudimentary end-point analysis. This negatively impacts both testing accuracy and testing times, ultimately lowering diagnostic throughput. Herein, we present an automated computational imaging method for processing and analysing multiple LFIAs in real-time and in parallel. This method relies on the automated detection of signal intensity at the test line, control line, and background, and employs statistical comparison of these values to predictively categorise tests as “positive”, “negative”, or “failed”. We show that such a computational methodology can be transferred to a smartphone and detail how real-time analysis of LFIAs can be leveraged to decrease the time-to-result and increase testing throughput. We compare our method to naked-eye readout and demonstrate a shorter time-to-result across a range of target antigen concentrations and fewer false negatives compared to human subjects at low antigen concentrations. RSC 2022-12-01 /pmc/articles/PMC9850356/ /pubmed/36741250 http://dx.doi.org/10.1039/d2sd00197g Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Colombo, Monika
Bezinge, Léonard
Rocha Tapia, Andres
Shih, Chih-Jen
de Mello, Andrew J.
Richards, Daniel A.
Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title_full Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title_fullStr Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title_full_unstemmed Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title_short Real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
title_sort real-time, smartphone-based processing of lateral flow assays for early failure detection and rapid testing workflows
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9850356/
https://www.ncbi.nlm.nih.gov/pubmed/36741250
http://dx.doi.org/10.1039/d2sd00197g
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