Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose.
PLoS One
; 16(6): e0252121, 2021.
Article
in English
| MEDLINE | ID: covidwho-1256036
ABSTRACT
Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Volatile Organic Compounds
/
SARS-CoV-2
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Variants
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
Asia
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pone.0252121
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