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

Zachary S Ballard,Hyou-Arm Joung,Artem Goncharov,Jesse Liang,Karina Nugroho,Dino Di Carlo,Omai B Garner,Aydogan Ozcan

doi:10.1038/s41746-020-0274-y

Zachary S Ballard, Hyou-Arm Joung + Show 6 more

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https://doi.org/10.1038/s41746-020-0274-y

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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 R2 = 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.

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Computation has great potential for improving diagnostics
Point-of-care (POC) testing can especially benefit from computational sensing approaches. Due to their low-cost materials, compact designs, and requirement for rapid and user-friendly operation, POC tests are often less accurate when compared to traditional laboratory tests and assays[7,8,9,10,11,12]. Paperbased immunoassays such as rapid diagnostic tests (RDTs) offer an affordable and user-friendly class of POC tests which have been developed for malaria, HIV-1/2, and cancer screening, among other uses[13,14,15,16,17]
As a demonstration of this emerging opportunity at the intersection of computational sensing and machine learning, we report a computational paper-based vertical flow assay (VFA) for cost-effective high-sensitivity C-reactive protein testing, referred to as cardiac CRP testing[35]

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Computation has great potential for improving diagnostics. By identifying complex and nonlinear patterns from noisy inputs, computational tools present an opportunity for automated and robust inference of medical data. Recent work by our group investigated the use of neural networks for POC Lyme disease diagnostics using a VFA format, achieving competitive results compared to the gold-standard clinical testing.[32] in contrast to this previous report, here we uniquely demonstrate (1) precise quantification of a protein biomarker as opposed to a binary (positive/negative) decision, (2) the incorporation of the test fabrication information into the learning model to improve quantitative sensing performance, and (3) a significantly extended sensing dynamic range through computational analysis of multiplexed immunoreaction spots, all targeting the same analyte in uniquely different ways.

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