SBIR Phase II: Machine Learning for Rapid Automated Viral Infectivity Assays (COVID-19)

Award ID: 2136850

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to accelerate the development of antiviral drugs and vaccines for conditions such as COVID-19. The ability to accurately determine if cells are infected with virus is crucial for evaluating antiviral drugs and vaccine candidates. Currently, determining if a virus is infectious is done by infecting cells and waiting for them to die, which can take many days. The proposed technology uses artificial intelligence (AI) to analyze images of cells for signs of virus. This can be done within hours of infection instead of days, which can greatly accelerate the development of vaccines and antiviral drugs. In addition, it is simpler because the AI analysis is automated and does not need special probes or dyes to detect viruses. The proposed project will collect images of cells infected with various viruses imaged with automated microscopes. AIs will be trained to distinguish healthy and sick cells at various times after infection. The project will study many different viruses and the changes they induce in cells. The trained AIs will be used to process infectivity assays. The software will run on remote data centers and thus images will be uploaded for analysis and reporting. An AI can be trained using images from the common 96-well plate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Erik Pierstorff