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Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors.
- Source :
- NPJ Digital Medicine; 5/7/2020, Vol. 3 Issue 1, p1-8, 8p
- Publication Year :
- 2020
-
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<superscript>2</superscript> = 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
C-reactive protein
POINT-of-care testing
MACHINE learning
DETECTORS
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 3
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 143095738
- Full Text :
- https://doi.org/10.1038/s41746-020-0274-y