New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning
Abstract Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS–CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:54 |
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| Enthalten in: |
Brazilian journal of microbiology - 54(2023), 2 vom: 28. Feb., Seite 769-777 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ceccon, Denny M. [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
© The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s42770-023-00923-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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OLC2143651546 |
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| 520 | |a Abstract Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS–CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future. | ||
| 650 | 4 | |a Optical spectroscopy | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Label-free diagnosis | |
| 700 | 1 | |a Amaral, Paulo Henrique R. |4 aut | |
| 700 | 1 | |a Andrade, Lídia M. |4 aut | |
| 700 | 1 | |a da Silva, Maria I. N. |4 aut | |
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| 700 | 1 | |a Ribeiro, Ágata Lopes |4 aut | |
| 700 | 1 | |a Coelho-dos-Reis, Jordana G. A. |4 aut | |
| 700 | 1 | |a da Fonseca, Flavio G. |4 aut | |
| 700 | 1 | |a Gonzalez, J. C. |0 (orcid)0000-0001-9155-1657 |4 aut | |
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