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

Gridsada Phanomchoeng; Chayatorn Kukiattikoon; Suphanut Plengkham; Siwaporn Boonyasuppayakorn; Saran Salakij; Suvit Poomrittigul; Lunchakorn Wuttisittikulkij

doi:10.7717/peerj-cs.878

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

PeerJ Comput Sci. 2022 Mar 4:8:e878. doi: 10.7717/peerj-cs.878. eCollection 2022.

Authors

Gridsada Phanomchoeng^{1

2}, Chayatorn Kukiattikoon¹, Suphanut Plengkham¹, Siwaporn Boonyasuppayakorn^{2

3}, Saran Salakij^{1

2}, Suvit Poomrittigul⁴, Lunchakorn Wuttisittikulkij⁵

Affiliations

¹ Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.
² Applied Medical Virology Research Unit, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.
³ Department of Microbiology, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.
⁴ Department of Software Engineering and Information System, Pathumwan Institute of Technology, Pathumwan Institute of Technology, Bangkok, Thailand.
⁵ Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.

Abstract

The plaque assay is a standard quantification system in virology for verifying infectious particles. One of the complex steps of plaque assay is the counting of the number of viral plaques in multiwell plates to study and evaluate viruses. Manual counting plaques are time-consuming and subjective. There is a need to reduce the workload in plaque counting and for a machine to read virus plaque assay; thus, herein, we developed a machine-learning (ML)-based automated quantification machine for viral plaque counting. The machine consists of two major systems: hardware for image acquisition and ML-based software for image viral plaque counting. The hardware is relatively simple to set up, affordable, portable, and automatically acquires a single image or multiple images from a multiwell plate for users. For a 96-well plate, the machine could capture and display all images in less than 1 min. The software is implemented by K-mean clustering using ML and unsupervised learning algorithms to help users and reduce the number of setup parameters for counting and is evaluated using 96-well plates of dengue virus. Bland-Altman analysis indicates that more than 95% of the measurement error is in the upper and lower boundaries [±2 standard deviation]. Also, gage repeatability and reproducibility analysis showed that the machine is capable of applications. Moreover, the average correct measurements by the machine are 85.8%. The ML-based automated quantification machine effectively quantifies the number of viral plaques.

Keywords: Image segmentation; Optical inspection; Viral plaque assay; Virus titer; Well plate; K-mean clustering.

Grants and funding

This research is funded by TSRI Fund (CU_FRB640001_01_21_8). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.