Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Journal: Small (Weinheim an der Bergstrasse, Germany)
PMID: 39910838

Abstract

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, high-sensitivity detection of cardiac troponin I (cTnI) is demonstrated through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertical flow assay (CL-VFA) enables rapid, low-cost, and precise quantification of cTnI, a key cardiac protein for assessing heart muscle damage and myocardial infarction. The CL-VFA integrates a user-friendly chemiluminescent paper-based sensor, a polymerized enzyme-based conjugate, a portable high-performance CL reader, and a neural network-based cTnI concentration inference algorithm. The CL-VFA measures cTnI over a broad dynamic range covering six orders of magnitude and operates with 50 µL of serum per test, delivering results in 25 min. This system achieves a detection limit of 0.16 pg mL with an average coefficient of variation under 15%, surpassing traditional benchtop analyzers in sensitivity by an order of magnitude. In blinded validation, the computational CL-VFA accurately measures cTnI concentrations in patient samples, demonstrating a robust correlation against a clinical-grade FDA-cleared analyzer. These results highlight the potential of CL-VFA as a robust diagnostic tool for accessible, rapid cardiac biomarker testing that meets the needs of diverse healthcare settings, from emergency care to underserved regions.

Authors

  • Gyeo-Re Han
    Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Artem Goncharov
    Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Merve Eryilmaz
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Shun Ye
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Hyou-Arm Joung
    Department of Electrical & Computer Engineering , University of California , Los Angeles , California 90025 , United States.
  • Rajesh Ghosh
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Emily Ngo
    Department of Psychology, University of California, Los Angeles, CA, 90095, USA.
  • Aoi Tomoeda
    Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Yena Lee
    Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada.
  • Kevin Ngo
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Elizabeth Melton
    Biomedical Engineering Department, University of California, Davis, CA, 95616, USA.
  • Omai B Garner
    Department of Pathology and Laboratory Medicine , University of California , Los Angeles , California 90025 , United States.
  • Dino Di Carlo
    2Department of Bioengineering, University of California, 420 Westwood Plaza, 5121 Engineering V, PO Box 951600, Los Angeles, CA 90095 USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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