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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Main Authors:	Zachary S. Ballard, Hyou-Arm Joung, Artem Goncharov, Jesse Liang, Karina Nugroho, Dino Di Carlo, Omai B. Garner, Aydogan Ozcan
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-05-01
Series:	npj Digital Medicine
Online Access:	https://doi.org/10.1038/s41746-020-0274-y

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record_format	Article
spelling	doaj-62e597e200f243a7b19c1eeece7eed142021-05-09T11:39:58ZengNature Publishing Groupnpj Digital Medicine2398-63522020-05-01311810.1038/s41746-020-0274-yDeep learning-enabled point-of-care sensing using multiplexed paper-based sensorsZachary S. Ballard0Hyou-Arm Joung1Artem Goncharov2Jesse Liang3Karina Nugroho4Dino Di Carlo5Omai B. Garner6Aydogan Ozcan7Department of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaCalifornia NanoSystems Institute, University of CaliforniaDepartment of Bioengineering, University of CaliforniaCalifornia NanoSystems Institute, University of CaliforniaDepartment of Pathology and Medicine, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaAbstract 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.https://doi.org/10.1038/s41746-020-0274-y
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Zachary S. Ballard Hyou-Arm Joung Artem Goncharov Jesse Liang Karina Nugroho Dino Di Carlo Omai B. Garner Aydogan Ozcan
spellingShingle	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 npj Digital Medicine
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 Publishing Group
series	npj Digital Medicine
issn	2398-6352
publishDate	2020-05-01
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.
url	https://doi.org/10.1038/s41746-020-0274-y
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Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors

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