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Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

Katherine Ember, François Daoust, Myriam Mahfoud, Frédérick Dallaire, Esmat Zamani Ahmad, Trang Tran, Arthur Plante, Mame-Kany Diop, Tien Nguyen, Amélie St-Georges-Robillard, Nassim Ksantini, Julie Lanthier, Antoine Filiatrault, Guillaume Sheehy, Gabriel Beaudoin, Caroline Quach, Dominique Trudel and Frédéric Leblond

Article (2022)

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Abstract

SIGNIFICANCE: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus. AIM: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents. APPROACH: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection. RESULTS: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. CONCLUSION: These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

Uncontrolled Keywords

*covid-19; Female; Humans; Indicators and Reagents; Machine Learning; Male; SARS-CoV-2; *Saliva; Sensitivity and Specificity; Spectrum Analysis, Raman; *Raman spectroscopy; *biofluids; *coronavirus disease-19; *screening;

Subjects: 3100 Physics > 3100 Physics
9000 Health sciences > 9000 Health sciences
Department: Department of Engineering Physics
Research Center: Other
Funders: Canada First Research Excellence Fund (TransMedTech Institute, IVADO), Natural Sciences and Engineering Research Council of Canada (Alliance and Discovery grant programs), Canada Foundation for Innovation (Exceptional Opportunities Fund program), TransMedTech Institute
PolyPublie URL: https://publications.polymtl.ca/10628/
Edition: 2022/02/11th ed.
Journal Title: Journal of Biomedical Optics (vol. 27, no. 2)
Publisher: SPIE
DOI: 10.1117/1.jbo.27.2.025002
Official URL: https://doi.org/10.1117/1.jbo.27.2.025002
Date Deposited: 18 Jul 2023 12:42
Last Modified: 08 Apr 2024 13:43
Cite in APA 7: Ember, K., Daoust, F., Mahfoud, M., Dallaire, F., Ahmad, E. Z., Tran, T., Plante, A., Diop, M.-K., Nguyen, T., St-Georges-Robillard, A., Ksantini, N., Lanthier, J., Filiatrault, A., Sheehy, G., Beaudoin, G., Quach, C., Trudel, D., & Leblond, F. (2022). Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning. Journal of Biomedical Optics, 27(2), 025002 (24 pages). https://doi.org/10.1117/1.jbo.27.2.025002

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