SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate the development of vaccines and anti-virals with artificial intelligence (AI) techniques. This project will develop technology to detect changes in virus-infected cells days or weeks before they can be detected manually. This will accelerate studies for novel anti-viral compounds characterizing their effectiveness on rapidly mutating viral strains, such as influenza and SARS-CoV-2. This will impact COVID-19 research and general virology.This SBIR Phase I project will investigate AI techniques to accelerate testing of anti-viral agents in plaque assays for the development of vaccines and anti-virals. These assays measure the number of infectious viral particles in a sample by observing the effects of infection on a culture of susceptible cells. Currently, the assay takes 2-14 days because several rounds of infection are necessary to ensure an accurate reading. This project will advance AI techniques to automatically detect infected cells in microscopy images without human intervention or time-consuming preparations, thereby increasing the throughput for these assays. To achieve this goal, this project will: 1) Collect a time-course of microscopy images of infected cell cultures for training an AI model to measure virus infections automatically on large cell culture plates; 2) Investigate microscopy image acquisition approaches with respect to ease of integration in existing workflows and image quality; 3) Evaluate the suitability of various AI techniques; 4) Determine the detection accuracy and compare it with traditional assays.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.

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.