Article

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

Zachary S. Ballard1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USAHyou‐Arm Joung1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USAArtem Goncharov1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USAJesse Liang2California NanoSystems Institute, University of California, Los Angeles, CA USAKarina Nugroho3Department of Bioengineering, University of California, Los Angeles, CA USADino Di Carlo2California NanoSystems Institute, University of California, Los Angeles, CA USAOmai B. Garner4Department of Pathology and Medicine, University of California, Los Angeles, CA USAAydogan Özcan1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USA

2020en

ABI

Abstract

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.

Identifiers

DOI: 10.1038/s41746-020-0274-y

Citations and references

Cited by 20 references

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