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

Zachary S Ballard1,2, Hyou-Arm Joung1,3, Artem Goncharov1

  • 11Department of Electrical and Computer Engineering, University of California, Los Angeles, CA USA.

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Summary

We developed a deep learning framework for designing point-of-care sensors, demonstrated with a rapid hsCRP test for cardiovascular disease risk. This computational vertical flow assay offers accessible, low-cost diagnostics.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Point-of-Care Diagnostics
  • Machine Learning Applications

Background:

  • Cardiovascular disease (CVD) risk assessment relies on biomarkers like high-sensitivity C-Reactive Protein (hsCRP).
  • Current hsCRP testing methods can be costly and inaccessible for widespread point-of-care (POC) use.
  • Developing rapid, low-cost, and accurate POC sensors is crucial for early CVD risk detection.

Purpose of the Study:

  • To present a deep learning framework for designing and quantifying POC sensors.
  • To demonstrate a paper-based vertical flow assay (VFA) for hsCRP detection.
  • To enable accurate analyte concentration inference and optimize sensor configuration.

Main Methods:

  • A machine learning framework was developed to optimize spatial arrangement and conditions of immunoreaction spots on a sensing membrane.
  • A custom handheld VFA reader was used for data acquisition.
  • Deep learning models were employed to infer hsCRP concentration from VFA output.

Main Results:

  • A paper-based VFA for hsCRP achieved a coefficient-of-variation of 11.2% and linearity of R²=0.95 across 85 human samples.
  • The assay demonstrated a wide dynamic range (0-10 mg/L) for hsCRP detection.
  • Multiplexed immunoreactions on the sensing membrane effectively mitigated the hook effect.

Conclusions:

  • The presented deep learning framework enables the design of cost-effective, mobile POC sensors.
  • The paper-based computational VFA offers a promising solution for accessible CVD risk testing.
  • This approach can be broadly applied to develop novel POC diagnostic tools.