New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning

Abstract

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS–CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future. Supplementary Information The online version contains supplementary material available at 10.1007/s42770-023-00923-5.

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“…The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines12010167/s1, Figure S1: Surface of the SERS platform (silicon micromachined via femtosecond laser and covered with 100 nm of silver) acquired by Scanning Electron Microscopy (SEM); Table S1: Tentative assignments for the main bands observed in the SERS spectra of SARS-CoV-2-infected CoV(+) and healthy CoV(−) subjects [51][52][53][54][55][56][57][58]; Table S2: The comparison of accuracy, sensitivity, and specificity for the analysis of body fluids in terms of COVID-19 diagnosis in a label-free manner [33,34,[59][60][61].…”

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