Virus detection and identification in minutes using single-particle imaging and deep learning

Shiaelis, N.; Tometzki, A.; Peto, L.; McMahon, A.; Hepp, C.; Bickerton, E.; Favard, C.; Muriaux, D.; Andersson, M.; Oakley, S.; Vaughan, A.; Matthews, P. C.; Stoesser, N.; Crook, D.; Kapanidis, A. N.; Robb, N. C.

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.