Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors

Abstract We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular dis...

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Autores principales:	Zachary S. Ballard, Hyou-Arm Joung, Artem Goncharov, Jesse Liang, Karina Nugroho, Dino Di Carlo, Omai B. Garner, Aydogan Ozcan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/62e597e200f243a7b19c1eeece7eed14
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spelling	oai:doaj.org-article:62e597e200f243a7b19c1eeece7eed142021-12-02T14:29:16ZDeep learning-enabled point-of-care sensing using multiplexed paper-based sensors10.1038/s41746-020-0274-y2398-6352https://doaj.org/article/62e597e200f243a7b19c1eeece7eed142020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0274-yhttps://doaj.org/toc/2398-6352Abstract We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R 2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0–10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors.Zachary S. BallardHyou-Arm JoungArtem GoncharovJesse LiangKarina NugrohoDino Di CarloOmai B. GarnerAydogan OzcanNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Zachary S. Ballard Hyou-Arm Joung Artem Goncharov Jesse Liang Karina Nugroho Dino Di Carlo Omai B. Garner Aydogan Ozcan Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
description	Abstract We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R 2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0–10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors.
format	article
author	Zachary S. Ballard Hyou-Arm Joung Artem Goncharov Jesse Liang Karina Nugroho Dino Di Carlo Omai B. Garner Aydogan Ozcan
author_facet	Zachary S. Ballard Hyou-Arm Joung Artem Goncharov Jesse Liang Karina Nugroho Dino Di Carlo Omai B. Garner Aydogan Ozcan
author_sort	Zachary S. Ballard
title	Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
title_short	Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
title_full	Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
title_fullStr	Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
title_full_unstemmed	Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
title_sort	deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/62e597e200f243a7b19c1eeece7eed14
work_keys_str_mv	AT zacharysballard deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT hyouarmjoung deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT artemgoncharov deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT jesseliang deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT karinanugroho deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT dinodicarlo deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT omaibgarner deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors AT aydoganozcan deeplearningenabledpointofcaresensingusingmultiplexedpaperbasedsensors
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Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors

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