Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning.
J Biomed Opt
; 27(2)2022 02.
Article
in English
| MEDLINE | ID: covidwho-1677373
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Saliva
/
COVID-19
Type of study:
Diagnostic study
Topics:
Variants
Limits:
Female
/
Humans
/
Male
Language:
English
Journal subject:
Biomedical Engineering
/
Ophthalmology
Year:
2022
Document Type:
Article
Affiliation country:
1.JBO.27.2.025002
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