@article{dodkins_delaney_overton_scholle_frias-de-diego_crisci_huq_jordan_kimata_findley_et al._2023, title={A rapid, high-throughput, viral infectivity assay using automated brightfield microscopy with machine learning}, volume={28}, ISSN={2472-6303}, url={http://dx.doi.org/10.1016/j.slast.2023.07.003}, DOI={10.1016/j.slast.2023.07.003}, abstractNote={Infectivity assays are essential for the development of viral vaccines, antiviral therapies, and the manufacture of biologicals. Traditionally, these assays take 2-7 days and require several manual processing steps after infection. We describe an automated viral infectivity assay (AVIATM), using convolutional neural networks (CNNs) and high-throughput brightfield microscopy on 96-well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation. CNN models were trained on HIV, influenza A virus, coronavirus 229E, vaccinia viruses, poliovirus, and adenoviruses, which together span the four major categories of virus (DNA, RNA, enveloped, and non-enveloped). A sigmoidal function, fit between virus dilution curves and CNN predictions, results in sensitivity ranges comparable to or better than conventional plaque or TCID50 assays, and a precision of ∼10%, which is considerably better than conventional infectivity assays. Because this technology is based on sensitizing CNNs to specific phenotypes of infection, it has potential as a rapid, broad-spectrum tool for virus characterization, and potentially identification.}, number={5}, journal={SLAS Technology}, publisher={Elsevier BV}, author={Dodkins, Rupert and Delaney, John R. and Overton, Tess and Scholle, Frank and Frias-De-Diego, Alba and Crisci, Elisa and Huq, Nafisa and Jordan, Ingo and Kimata, Jason T. and Findley, Teresa and et al.}, year={2023}, month={Jul}, pages={324–333} }