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Machine-learning-based automated quantification machine for virus plaque assay counting.

Gridsada Phanomchoeng1,2, Chayatorn Kukiattikoon1, Suphanut Plengkham1

  • 1Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated machine using machine learning (ML) for viral plaque counting, significantly reducing manual workload. The developed system offers a faster, more objective method for quantifying infectious viral particles in plaque assays.

Keywords:
Image segmentationOptical inspectionViral plaque assayVirus titerWell plateK-mean clustering

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Area of Science:

  • Virology
  • Machine Learning
  • Image Analysis

Background:

  • Plaque assays are crucial for quantifying infectious viral particles in virology.
  • Manual plaque counting is labor-intensive, time-consuming, and prone to subjectivity.
  • Automated solutions are needed to improve efficiency and objectivity in viral quantification.

Purpose of the Study:

  • To develop and evaluate a machine-learning-based automated quantification machine for viral plaque counting.
  • To reduce the workload and subjectivity associated with manual plaque counting.
  • To provide a rapid and accurate method for viral plaque enumeration.

Main Methods:

  • Development of a two-part system: hardware for image acquisition and ML-based software for plaque counting.
  • Utilized K-mean clustering and unsupervised learning algorithms for automated image analysis.
  • Evaluated the system using dengue virus plaque assays in 96-well plates.

Main Results:

  • The automated system captures images from a 96-well plate in under 1 minute.
  • Bland-Altman analysis showed >95% of measurement error within ±2 standard deviations.
  • The machine achieved an average correct measurement rate of 85.8%.

Conclusions:

  • The developed machine-learning-based automated quantification machine effectively and accurately quantifies viral plaques.
  • This automated system offers a viable solution to overcome the limitations of manual plaque counting.
  • The machine provides a simple, affordable, and portable tool for virological research.