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Related Experiment Video

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PhoneQuant: A smartphone-based quantitative immunoassay analyser.

Malay Ilesh Shah, Jayaraj Joseph, Ujwal Sriharsha Sanne

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new smartphone-based analyzer offers a portable and cost-effective solution for quantitative Lateral Flow Immunoassays (LFIA). This device enables rapid point-of-care (PoC) testing, crucial for diagnosing non-communicable diseases like diabetes, especially in remote areas.

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    Area of Science:

    • Biomedical Engineering
    • Point-of-Care Diagnostics
    • Medical Devices

    Background:

    • The increasing global burden of non-communicable diseases (NCDs), particularly diabetes, necessitates accessible diagnostic tools in resource-limited settings.
    • Traditional laboratory-based diagnostic methods for NCDs are often costly, time-consuming, and inaccessible for point-of-care (PoC) applications.
    • Lateral Flow Immunoassays (LFIA) offer potential for rapid, field-deployable diagnostics, but quantitative analysis often requires bulky equipment.

    Purpose of the Study:

    • To develop and validate a smartphone-based, fully quantitative analyzer for Lateral Flow Immunoassays (LFIA).
    • To create a portable, cost-effective alternative to conventional LFIA analysis systems.
    • To assess the performance of the smartphone-based analyzer for Glycated Haemoglobin (HbA1c) quantification in blood samples.

    Main Methods:

    • Design and implementation of a smartphone-based analyzer integrated with an automatic image processing algorithm.
    • Conducting repeatability studies using stable fluorescence reference cartridges to determine the Coefficient of Variation (CoV).
    • Generating calibration curves for HbA1c using blood samples against a standard laboratory instrument, with varying smartphone camera settings.

    Main Results:

    • Repeatability studies demonstrated good performance with a Coefficient of Variation (CoV) below 1.5%.
    • Calibration curves generated using three different smartphone camera settings showed excellent linear correlation, with R-squared values exceeding 0.985.
    • Validation using HbA1c blood samples confirmed the reliability of the system, with CoV consistently below 5% for each sample.

    Conclusions:

    • The developed smartphone-based LFIA analyzer, PhoneQuant, provides a portable and cost-effective solution for quantitative diagnostics.
    • The system demonstrates high accuracy and reliability, suitable for point-of-care testing in diverse settings, including physician's offices and home use.
    • This technology holds significant potential for improving the accessibility and efficiency of diagnosing NCDs like diabetes, particularly in underserved regions.