Rapid and stain-free quantification of viral plaques using deep learning and time-lapse holographic imaging

Yuzhu Li; Tairan Liu; Hatice C. Koydemir; Yijie Zhang; Ethan Yang; Hongda Wang; Jingxi Li; Bijie Bai; Aydogan Ozcan

doi:10.1117/12.3027879

4 October 2024 Rapid and stain-free quantification of viral plaques using deep learning and time-lapse holographic imaging

Yuzhu Li, Tairan Liu, Hatice C. Koydemir, Yijie Zhang, Ethan Yang, Hongda Wang, Jingxi Li, Bijie Bai, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180I (2024) https://doi.org/10.1117/12.3027879
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We present a rapid, stain-free, and automated viral plaque assay utilizing deep learning and time-lapse holographic imaging, which can significantly reduce the time needed for plaque-forming unit (PFU) detection and entirely bypass the chemical staining and manual counting processes. Demonstrated with vesicular stomatitis virus (VSV), our system identified the first PFU events as early as 5 hours of incubation and detected >90% of PFUs with 100% specificity in <20 hours, saving >24 hours compared to the traditional viral plaque assays that take ≥48 hours. Furthermore, our method was proven to adapt seamlessly to new types of viruses by transfer learning.

Conference Presentation

Citation Download Citation

Yuzhu Li, Tairan Liu, Hatice C. Koydemir, Yijie Zhang, Ethan Yang, Hongda Wang, Jingxi Li, Bijie Bai, and Aydogan Ozcan "Rapid and stain-free quantification of viral plaques using deep learning and time-lapse holographic imaging", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180I (4 October 2024); https://doi.org/10.1117/12.3027879

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