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

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Title: Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning.
Authors: Ember, Katherine, Daoust, François, Mahfoud, Myriam, Dallaire, Frédérick, Ahmad, Esmat Zamani, Tran, Trang, Plante, Arthur, Diop, Mame-Kany, Nguyen, Tien, St-Georges-Robillard, Amélie, Ksantini, Nassim, Lanthier, Julie, Filiatrault, Antoine, Sheehy, Guillaume, Beaudoin, Gabriel, Quach, Caroline, Trudel, Dominique, Leblond, Frédéric
Source: Journal of Biomedical Optics; Feb2022, Vol. 27 Issue 2, p25002-25002, 1p
Subject Terms: SALIVA, COVID-19, RAMAN spectroscopy, REVERSE transcriptase polymerase chain reaction, MACHINE learning
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Biomedical Optics is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning.
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  Data: Journal of Biomedical Optics; Feb2022, Vol. 27 Issue 2, p25002-25002, 1p
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  Data: <searchLink fieldCode="DE" term="%22SALIVA%22">SALIVA</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22RAMAN+spectroscopy%22">RAMAN spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22REVERSE+transcriptase+polymerase+chain+reaction%22">REVERSE transcriptase polymerase chain reaction</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Biomedical Optics is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1117/1.JBO.27.2.025002
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        Text: English
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      – SubjectFull: COVID-19
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      – SubjectFull: RAMAN spectroscopy
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      – SubjectFull: REVERSE transcriptase polymerase chain reaction
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      – SubjectFull: MACHINE learning
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              Text: Feb2022
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