Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and 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. Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. Approach: We present a workflow for collecting, preparing and imaging dried saliva supernatant droplets using a non-invasive, label-free technique known as Raman spectroscopy to detect changes in the molecular profile of saliva associated with COVID-19 infection. Results: Using machine learning and droplet segmentation, amongst all confounding factors, we discriminated between COVID-positive and negative individuals yielding receiver operating coefficient (ROC) curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity, 75% specificity) and females (84% sensitivity, 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.

Research progress of artificial intelligence technology in COVID-19

“…With the emergence of new variants of the coronavirus, the sensitivity of PCR testing may decrease, thereby lowering the specificity of reagents for virus detection. As a result, some scholars are striving to achieve a minimal sample size and reagent-free method for COVID-19 nucleic acid testing [20,21] . Ember K et al [20] proposed a workflow for detecting changes in molecular spectra related to saliva in COVID-19 patients by collecting and preparing dried saliva droplets.…”

“…As a result, some scholars are striving to achieve a minimal sample size and reagent-free method for COVID-19 nucleic acid testing [20,21] . Ember K et al [20] proposed a workflow for detecting changes in molecular spectra related to saliva in COVID-19 patients by collecting and preparing dried saliva droplets. Using saliva samples from 37 COVID-19 patients and 513 negative patients obtained according to collection standards, molecular spectra data were recorded by Raman spectrometry and classified using a machine learning method (support vector machine).…”