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Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19
BACKGROUND: Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mas...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Respiratory Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505950/ https://www.ncbi.nlm.nih.gov/pubmed/37727677 http://dx.doi.org/10.1183/23120541.00206-2023 |
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author | Roquencourt, Camille Salvator, Hélène Bardin, Emmanuelle Lamy, Elodie Farfour, Eric Naline, Emmanuel Devillier, Philippe Grassin-Delyle, Stanislas |
author_facet | Roquencourt, Camille Salvator, Hélène Bardin, Emmanuelle Lamy, Elodie Farfour, Eric Naline, Emmanuel Devillier, Philippe Grassin-Delyle, Stanislas |
author_sort | Roquencourt, Camille |
collection | PubMed |
description | BACKGROUND: Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19. METHODS: In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata. RESULTS: We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status. CONCLUSIONS: Real-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening. |
format | Online Article Text |
id | pubmed-10505950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | European Respiratory Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-105059502023-09-19 Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 Roquencourt, Camille Salvator, Hélène Bardin, Emmanuelle Lamy, Elodie Farfour, Eric Naline, Emmanuel Devillier, Philippe Grassin-Delyle, Stanislas ERJ Open Res Original Research Articles BACKGROUND: Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19. METHODS: In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata. RESULTS: We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status. CONCLUSIONS: Real-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening. European Respiratory Society 2023-09-18 /pmc/articles/PMC10505950/ /pubmed/37727677 http://dx.doi.org/10.1183/23120541.00206-2023 Text en Copyright ©The authors 2023 https://creativecommons.org/licenses/by-nc/4.0/This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org (mailto:permissions@ersnet.org) |
spellingShingle | Original Research Articles Roquencourt, Camille Salvator, Hélène Bardin, Emmanuelle Lamy, Elodie Farfour, Eric Naline, Emmanuel Devillier, Philippe Grassin-Delyle, Stanislas Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title | Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title_full | Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title_fullStr | Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title_full_unstemmed | Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title_short | Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19 |
title_sort | enhanced real-time mass spectrometry breath analysis for the diagnosis of covid-19 |
topic | Original Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505950/ https://www.ncbi.nlm.nih.gov/pubmed/37727677 http://dx.doi.org/10.1183/23120541.00206-2023 |
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