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
Abstract 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 s...
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Wiley
2024-11-01
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| Online Access: | https://doi.org/10.1002/mco2.726 |
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| author | Tobias Woehrle Florian Pfeiffer Maximilian M. Mandl Wolfgang Sobtzick Jörg Heitzer Alisa Krstova Luzie Kamm Matthias Feuerecker Dominique Moser Matthias Klein Benedikt Aulinger Michael Dolch Anne‐Laure Boulesteix Daniel Lanz Alexander Choukér |
| author_facet | Tobias Woehrle Florian Pfeiffer Maximilian M. Mandl Wolfgang Sobtzick Jörg Heitzer Alisa Krstova Luzie Kamm Matthias Feuerecker Dominique Moser Matthias Klein Benedikt Aulinger Michael Dolch Anne‐Laure Boulesteix Daniel Lanz Alexander Choukér |
| author_sort | Tobias Woehrle |
| collection | DOAJ |
| description | Abstract 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 (n = 63) or without SARS‐CoV‐2 pneumonia (n = 63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E‐Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi‐analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E‐Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point‐of‐care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing. |
| format | Article |
| id | doaj-art-35d1bf2a01ba4ed990e24c3392fd6c9d |
| institution | Kabale University |
| issn | 2688-2663 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | MedComm |
| spelling | doaj-art-35d1bf2a01ba4ed990e24c3392fd6c9d2024-11-14T16:14:36ZengWileyMedComm2688-26632024-11-01511n/an/a10.1002/mco2.726Point‐of‐care breath sample analysis by semiconductor‐based E‐Nose technology discriminates non‐infected subjects from SARS‐CoV‐2 pneumonia patients: a multi‐analyst experimentTobias Woehrle0Florian Pfeiffer1Maximilian M. Mandl2Wolfgang Sobtzick3Jörg Heitzer4Alisa Krstova5Luzie Kamm6Matthias Feuerecker7Dominique Moser8Matthias Klein9Benedikt Aulinger10Michael Dolch11Anne‐Laure Boulesteix12Daniel Lanz13Alexander Choukér14Department of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyInstitute for Medical Information Processing Biometry and Epidemiology Faculty of Medicine Ludwig Maximilian University Munich GermanyLANZ GmbH Bergisch Gladbach GermanyAirbus Defence and Space GmbH Claude‐Dornier‐Straße Immenstaad GermanyAirbus Defence and Space GmbH Claude‐Dornier‐Straße Immenstaad GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyEmergency Department LMU University Hospital Ludwig Maximilian University Munich GermanyDepartment of Medicine II LMU University Hospital Ludwig Maximilian University Munich GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyInstitute for Medical Information Processing Biometry and Epidemiology Faculty of Medicine Ludwig Maximilian University Munich GermanyLANZ GmbH Bergisch Gladbach GermanyDepartment of Anesthesiology LMU University Hospital Ludwig Maximilian University Munich GermanyAbstract 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 (n = 63) or without SARS‐CoV‐2 pneumonia (n = 63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E‐Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi‐analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E‐Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point‐of‐care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing.https://doi.org/10.1002/mco2.726breath gasCOVID‐19E‐Nosemachine learningmass spectrometrymetal oxide sensor |
| spellingShingle | Tobias Woehrle Florian Pfeiffer Maximilian M. Mandl Wolfgang Sobtzick Jörg Heitzer Alisa Krstova Luzie Kamm Matthias Feuerecker Dominique Moser Matthias Klein Benedikt Aulinger Michael Dolch Anne‐Laure Boulesteix Daniel Lanz Alexander Choukér 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 MedComm breath gas COVID‐19 E‐Nose machine learning mass spectrometry metal oxide sensor |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | breath gas COVID‐19 E‐Nose machine learning mass spectrometry metal oxide sensor |
| url | https://doi.org/10.1002/mco2.726 |
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