Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
04
09
2020
accepted:
10
05
2021
entrez:
2
6
2021
pubmed:
3
6
2021
medline:
29
6
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34077435
doi: 10.1371/journal.pone.0252121
pii: PONE-D-20-27895
pmc: PMC8172018
doi:
Substances chimiques
Volatile Organic Compounds
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0252121Subventions
Organisme : European Research Council
Pays : International
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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