Integrated System for On-Site Rapid and Safe Screening of COVID-19

Abstract

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

“…A partial least squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset of serum samples. The sensitivity, specificity and accuracy all reach over 94% from the blind test samples [46] . Zhang et al reported a rapid and sensitive magneto fluidic immune-PCR platform that can address the current gap in POC serological testing for COVID-19.…”