Phanomchoeng Gridsada, Kukiattikoon Chayatorn, Plengkham Suphanut, Boonyasuppayakorn Siwaporn, Salakij Saran, Poomrittigul Suvit, Wuttisittikulkij Lunchakorn
Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.
Applied Medical Virology Research Unit, Chulalongkorn University, Chulalongkorn University, Bangkok, Thailand.
PeerJ Comput Sci. 2022 Mar 4;8:e878. doi: 10.7717/peerj-cs.878. eCollection 2022.
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.
噬斑测定法是病毒学中用于验证感染性颗粒的标准定量系统。噬斑测定法复杂的步骤之一是对多孔板中的病毒噬斑数量进行计数，以研究和评估病毒。手动计数噬斑既耗时又主观。因此，需要减少噬斑计数的工作量，并让机器读取病毒噬斑测定结果；在此，我们开发了一种基于机器学习（ML）的自动定量机器用于病毒噬斑计数。该机器由两个主要系统组成：用于图像采集的硬件和基于ML的用于图像病毒噬斑计数的软件。硬件设置相对简单、价格实惠、便于携带，并且可以自动为用户从多孔板中获取单张图像或多张图像。对于96孔板，该机器能够在不到1分钟的时间内捕获并显示所有图像。该软件通过使用ML和无监督学习算法的K均值聚类来实现，以帮助用户并减少计数所需设置的参数数量，并使用登革热病毒的96孔板进行评估。Bland-Altman分析表明，超过95%的测量误差在上下边界[±2标准差]范围内。此外，量具重复性和再现性分析表明该机器能够应用。而且，该机器的平均正确测量率为85.8%。基于ML的自动定量机器有效地对病毒噬斑数量进行了定量。
