Machine learning in point-of-care testing: innovations, challenges, and opportunities.

Journal: Nature communications
PMID: 40175414

Abstract

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

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.
  • Barath Palanisamy
    Bioengineering Department, University of California, Los Angeles, CA, USA.
  • Rajesh Ghosh
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Fabio Lisi
    Department of Chemistry, The University of Tokyo, Tokyo, Japan.
  • Elliott Rogers
    London Centre for Nanotechnology and Division of Medicine, University College London, London, UK.
  • David Guzman
    London Centre for Nanotechnology and Division of Medicine, University College London, London, UK.
  • Defne Yigci
    Department of Mechanical Engineering, Koç University, Istanbul, Türkiye.
  • Savas Tasoglu
    Department of Mechanical Engineering, Koç University, Sariyer, Istanbul, 34450 Turkey. stasoglu@ku.edu.tr and Koç University Research Center for Translational Medicine, Koç University, Sariyer, Istanbul, 34450 Turkey and Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul, 34450 Turkey and Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul, 34684 Turkey.
  • Dino Di Carlo
    2Department of Bioengineering, University of California, 420 Westwood Plaza, 5121 Engineering V, PO Box 951600, Los Angeles, CA 90095 USA.
  • Keisuke Goda
  • Rachel A McKendry
    London Centre for Nanotechnology, University College London, London, UK. r.a.mckendry@ucl.ac.uk.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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