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ECLStat: A robust machine learning based visual imaging tool for electrochemiluminescence biosensing.

Abhishek Kumar1, Shashwat Goel2, Sanket Goel1

  • 1MEMS, Microfluidics and Nanoelectronics (MMNE) Lab, Birla Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Hyderabad 500078, India; Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Hyderabad 500078, India.

Computers in Biology and Medicine
|December 10, 2024
PubMed
Summary

A new machine learning application automates visual electrochemiluminescence (ECL) signal analysis, improving accuracy for disease marker detection like hydrogen peroxide and glucose. This advancement offers standardization and efficiency for point-of-care diagnostics.

Keywords:
BiomarkersBiosensingElectrochemiluminescence (ECL)Machine learningPoint of care

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Machine Learning Applications

Background:

  • Visual electrochemiluminescence (ECL) is a sensitive diagnostic technique for disease markers, suitable for point-of-care settings.
  • Current ECL methods lack standardization and accuracy due to manual data analysis, hindering real-time applications.
  • Existing ECL systems often require complex instrumentation, limiting accessibility.

Purpose of the Study:

  • To develop a fully automated, machine learning-assisted graphical user interface (GUI) application for ECL signal measurement and management.
  • To enhance the accuracy and standardization of ECL-based quantification of biomarkers.
  • To create a user-friendly platform for applying machine learning to raw ECL image data.

Main Methods:

  • Development of a standalone GUI application integrating machine learning algorithms for ECL signal processing.
  • Validation of the application's performance by detecting hydrogen peroxide (H₂O₂) and glucose.
  • Utilizing open-source image processing principles within an automated framework.

Main Results:

  • The developed application achieved a detection limit of 0.024 mM for H₂O₂ and 0.035 mM for glucose.
  • Quantification limits were established at 0.074 mM for H₂O₂ and 0.10 mM for glucose.
  • Demonstrated real-time utility and improved accuracy in ECL signal measurement.

Conclusions:

  • The automated GUI application standardizes ECL measurements, enhancing accuracy and efficiency.
  • The platform enables seamless application of machine learning for ECL data analysis, benefiting users without deep computational expertise.
  • The developed system has potential applications in other optical detection methods like chemiluminescence, colorimetric, and fluorescence assays.