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

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

• K-mean clustering
• Image segmentation
• Optical inspection
• Viral plaque assay
• Virus titer
• Well plate