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IQVision: An Image-Based Evaluation Tool for Quantitative Lateral Flow Immunoassay Kits.

Lalitha Pratyusha Bheemavarapu1, Malay Ilesh Shah2, Jayaraj Joseph1

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|July 2, 2021
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Summary

This study introduces an image-based tool to evaluate quantitative lateral flow immunoassay (LFIA) test strips. The tool analyzes sample flow, reaction stability, and detects strip abnormalities, aiding in the design of more reliable diagnostic tests.

Keywords:
IQVisionfluorescence imagingimage quantmedical diagnosticspoint-of-care technologyquantitative lateral flow assays

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Diagnostic Assay Development

Background:

  • Quantitative lateral flow immunoassay (LFIA) test strip development requires extensive parameter evaluation by kit manufacturers.
  • Key parameters include test region placement, sample flow dynamics, volume requirements, and reaction stability.
  • A practical visualization tool is needed to enhance the design of sensitive and reliable LFIA test strips.

Purpose of the Study:

  • To present a novel image-based quantitative evaluation tool for assessing the functionality of fluorescence-labelled LFIA test cartridges.
  • To develop image processing algorithms for practical analysis of LFIA performance parameters.

Main Methods:

  • Development of image processing algorithms to analyze sample flow rates and reaction stability times.
  • Implementation of algorithms for detecting abnormalities in LFIA test strips.
  • Evaluation of the tool using Glycated Hemoglobin (HbA1C) and Vitamin D test cartridges.

Main Results:

  • Demonstrated practical sample flow progress analysis for HbA1C test cartridges.
  • Determined reaction stability times: 12 minutes for HbA1C and 24 minutes for Vitamin D.
  • Achieved 100% accuracy in detecting sample flow abnormalities and 96% accuracy in detecting membrane irregularities.

Conclusions:

  • The presented image-based tool provides a practical method for evaluating quantitative LFIA test cartridges.
  • The developed algorithms effectively analyze key performance parameters and detect strip abnormalities, supporting improved diagnostic test design.