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

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

NPJ Digit Med. 2020 May 7:3:66. doi: 10.1038/s41746-020-0274-y. eCollection 2020.

Authors

Zachary S Ballard^{1

2}, Hyou-Arm Joung^{1

3}, Artem Goncharov¹, Jesse Liang^{2

3}, Karina Nugroho³, Dino Di Carlo^{2

3}, Omai B Garner⁴, Aydogan Ozcan^{1

2

3}

Affiliations

¹ 1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USA.
² 2California NanoSystems Institute, University of California, Los Angeles, CA USA.
³ 3Department of Bioengineering, University of California, Los Angeles, CA USA.
⁴ 4Department of Pathology and Medicine, University of California, Los Angeles, CA USA.

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.

Keywords: Assay systems; Cardiovascular diseases; Diagnostic markers; Machine learning; Optical sensors.