Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.
COVID‐19
E‐Nose
breath gas
machine learning
mass spectrometry
metal oxide sensor
pneumonia
volatile organic compounds
Journal
MedComm
ISSN: 2688-2663
Titre abrégé: MedComm (2020)
Pays: China
ID NLM: 101769925
Informations de publication
Date de publication:
Nov 2024
Nov 2024
Historique:
received:
26
02
2024
revised:
19
07
2024
accepted:
25
07
2024
medline:
28
10
2024
pubmed:
28
10
2024
entrez:
28
10
2024
Statut:
epublish
Résumé
Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment. Breath samples of 126 subjects with (
Identifiants
pubmed: 39465142
doi: 10.1002/mco2.726
pii: MCO2726
pmc: PMC11502717
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e726Informations de copyright
© 2024 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.
Déclaration de conflit d'intérêts
A.K. and J.H. are employees of Airbus Defense & Space, and authors W.S. and D.L. are part of Lanz GmbH, and have no potential relevant financial or non‐financial interests to disclose. The other authors have no conflicts of interest to declare.