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Area of Science:

  • Biomedical Engineering
  • Infectious Disease Diagnostics
  • Point-of-Care Testing

Background:

  • Point-of-Care Testing (POCT) is expanding, with Lateral Flow Assays (LFAs) offering rapid, cost-effective diagnostics.
  • Traditional LFAs often provide only qualitative or semiquantitative results, limiting their diagnostic utility.
  • Integrating Artificial Intelligence (AI) and image analysis enhances LFA accuracy, automation, and quantification.

Purpose of the Study:

  • To develop a smartphone and machine vision-driven multicolor LFA system for pathogen detection.
  • To create an independent AI tool to improve diagnostic accuracy and address challenges in smartphone-based LFAs.
  • To demonstrate multiplex detection of pathogens like *E. coli* and SARS-CoV-2 in a single test.

Main Methods:

  • Development of a smartphone-based multicolor Lateral Flow Assay (LFA).
  • Implementation of machine vision algorithms for automated image analysis and result interpretation.
  • Training of AI algorithms to correlate visual LFA results with analyte presence.
  • Validation of the system using real-world samples for pathogen detection.

Main Results:

  • Successful demonstration of a smartphone and AI-driven multicolor LFA system.
  • Accurate and multiplex detection of pathogens, including *E. coli* and SARS-CoV-2, in single tests.
  • Automated results presented via color, text, and audio messages, enhancing accessibility.
  • The system effectively addressed challenges like strip positioning and lighting variations inherent in smartphone-based diagnostics.

Conclusions:

  • The developed smartphone and AI-integrated LFA system significantly advances infectious disease diagnostics.
  • This technology offers a powerful, accessible, and automated solution for point-of-care pathogen detection.
  • The multiplexing capability and user-friendly output formats cater to diverse user needs and improve diagnostic workflows.