The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89–92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.
【저자키워드】 Diagnostic markers, Computational biology and bioinformatics, Predictive markers, Data processing, 【초록키워드】 COVID-19, Saliva, Efficacy, Infection, diagnostic, learning, Accuracy, Sensitivity and specificity, Patient, epidemiological, COVID-19 patients, Analysis, COVID-19 patient, Precision, biochemical, creation, subject, healthy subjects, signal, modifications, Modification, pandemic of COVID-19, regions, implication, approach, invasive, deep, collected, significantly, demonstrated, groups, translate, healthy subject, 【제목키워드】 COVID-19, SARS-COV-2 infection, salivary, approach,