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A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones.

Shenglan Zhang1, Xincheng Jiang1, Siqi Lu2

  • 1Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces a stable, accurate smartphone-based quantitative detection method for lateral flow immunoassay (LFIA) strips using image processing. The novel approach enhances diagnostic stability and simplifies quantitative analysis for improved results.

Keywords:
immunosensorslateral flow immunoassaymachine visionsmartphone applicationsupport vector machine

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Medical Diagnostics

Background:

  • Traditional lateral flow immunoassay (LFIA) methods exhibit limitations in detection stability and quantitative accuracy.
  • Existing LFIA techniques often require complex instrumentation or subjective interpretation.
  • There is a need for improved, user-friendly quantitative detection systems in point-of-care diagnostics.

Purpose of the Study:

  • To develop a novel, smartphone-based quantitative detection method for LFIA strips.
  • To enhance the stability and accuracy of LFIA results through advanced image processing.
  • To create a low-complexity algorithm for quantitative analysis of LFIA data.

Main Methods:

  • A multi-test line LFIA quantitative detection strategy was employed.
  • Smartphone imaging with controlled lighting in a dark box was utilized for sample capture.
  • Image processing algorithms were developed to analyze pigment intensity on LFIA strips.
  • Algorithm robustness was tested against introduced noise and varying smartphone specifications.

Main Results:

  • The proposed method achieved a 94.23% accuracy rate across 260 tested samples.
  • Quantitative analysis demonstrated higher stability compared to traditional LFIA methods.
  • The developed image processing algorithm exhibited lower complexity.
  • The system proved robust under varied conditions and smartphone types.

Conclusions:

  • Smartphone-based image processing offers a stable and accurate quantitative detection solution for LFIA.
  • This novel method provides a viable alternative to traditional LFIA, improving diagnostic reliability.
  • The approach holds potential for simplified, low-cost point-of-care diagnostic applications.