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13 March 2024 Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy
Katherine J. Ember, Nassim Ksantini, Frédéric Dallaire, Guillaume Sheehy, Antoine Noé, Trang Tran, Madeleine Durand, Dominique Trudel, Frederic Leblond
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Proceedings Volume PC12850, Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics; PC1285005 (2024) https://doi.org/10.1117/12.3001877
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
We present a rapid, portable optical system for label-free detection of COVID-19. Raman spectra from an entire liquid drop of saliva supernatant can be obtained within 6 minutes, and the sample is classified as COVID-19 positive or negative using artificial intelligence (AI). 293 COVID negative and 49 COVID positive saliva supernatant samples were analyzed. Positive samples were from hospitalized patients (non-critical and critical) and non-hospitalized testing clinic volunteers (symptomatic and asymptomatic). Our Raman/AI system has an 82% accuracy detecting people with COVID-19 of any severity with any symptom presentation, and 89% accuracy when detecting COVID-19 in hospitalized patients alone. Rapid label-free analysis of biofluids for viruses could provide a low-cost screening solution that could be adapted to respond to viral mutations. This could be invaluable for future pandemics and for reducing infections in hospitals, care homes and workplaces.
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Katherine J. Ember, Nassim Ksantini, Frédéric Dallaire, Guillaume Sheehy, Antoine Noé, Trang Tran, Madeleine Durand, Dominique Trudel, and Frederic Leblond "Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy", Proc. SPIE PC12850, Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics, PC1285005 (13 March 2024); https://doi.org/10.1117/12.3001877
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KEYWORDS
COVID 19
Artificial intelligence
Portability
Raman spectroscopy
Statistical analysis
Statistical modeling
Machine learning
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