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- Title
Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning.
- Authors
Nogueira, Marcelo Saito; Leal, Leonardo Barbosa; Macarini, Wena; Pimentel, Raquel Lemos; Muller, Matheus; Vassallo, Paula Frizera; Campos, Luciene Cristina Gastalho; dos Santos, Leonardo; Luiz, Wilson Barros; Mill, José Geraldo; Barauna, Valerio Garrone; de Carvalho, Luis Felipe das Chagas e Silva
- Abstract
Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.
- Subjects
COVID-19 testing; MACHINE learning; ATTENUATED total reflectance; COVID-19; HIGH throughput screening (Drug development)
- Publication
Scientific Reports, 2021, Vol 11, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-021-93511-2