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  • Machine Learning-enabled Classification and Monitoring of COVID-19 Immunity Levels Using a Paper-based Multiplexed Sensor
Title: Machine Learning-enabled Classification and Monitoring of COVID-19 Immunity Levels Using a Paper-based Multiplexed Sensor
To track COVID-19 immunity efficiently in point-of-care (POC) settings, we present a paper-based multiplexed vertical flow immunoassay (xVFA) along with a custom-designed serodiagnostic algorithm. During the development and testing of our algorithm, we utilized serum samples from individuals who had received mRNA-COVID-19 vaccines, tracking their antibody levels before and after each vaccine dose. By categorizing these samples based on their IgM and IgG levels into three categories (i.e., protected, unprotected, and infected), we trained and blindly evaluated a neural network-based algorithm for its inference accuracy. Leveraging this serodiagnostic algorithm, our cost-effective, paper-based xVFA platform swiftly measured the IgG and IgM levels from serum samples, facilitating the accurate monitoring of COVID-19 immunity levels. With its simple operation, scalability, and cost-effectiveness, our xVFA technology offers accessible COVID-19 serology testing to classify patients' immunity status rapidly.  more » « less
Award ID(s):
2149551
PAR ID:
10650395
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
BMES (Biomedical Engineering Society) Annual Meeting
Date Published:
Format(s):
Medium: X
Location:
Baltimore, MD, USA
Sponsoring Org:
National Science Foundation
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