Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning.
Sci Rep
; 11(1): 15409, 2021 10 11.
Article
in English
| MEDLINE | ID: covidwho-1462018
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Spectroscopy, Fourier Transform Infrared
/
COVID-19 Testing
/
SARS-CoV-2
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-93511-2
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