Award Abstract #2029707

SBIR Phase I: Machine Learning for Early Detection of COVID-19 Plaques in Cells

See grant description on NSF site

Program Manager:

Erik Pierstorff

Active Dates:

Awarded Amount:



Ilya G Goldberg

Awardee Organization:



Engineering (ENG)


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.

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