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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.19211 |
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| _version_ | 1866917795917201408 |
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| author | Bellarmino, Nicolo Bozzini, Giorgio Cantoro, Riccardo Castelletti, Francesco Castelluzzo, Michele Ciricugno, Carla Correale, Raffaele Gasperina, Daniela Dalla Dentali, Francesco Poggialini, Giovanni Salerno, Piergiorgio Squillero, Giovanni Taborelli, Stefano |
| author_facet | Bellarmino, Nicolo Bozzini, Giorgio Cantoro, Riccardo Castelletti, Francesco Castelluzzo, Michele Ciricugno, Carla Correale, Raffaele Gasperina, Daniela Dalla Dentali, Francesco Poggialini, Giovanni Salerno, Piergiorgio Squillero, Giovanni Taborelli, Stefano |
| contents | The SARS-CoV-2 coronavirus emerged in 2019, causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next four years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive detection system, which exploits only the exhaled breath. Specifically, provided an air sample, the mass spectra in the 10--351 mass-to-charge range are measured using an original nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on some 300 subjects who were concerned about being infected, either due to exhibiting symptoms or having quite recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 0.95 accuracy, 0.94 recall, 0.96 specificity, and 0.92 F1-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_19211 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | COVID-19 Detection from Exhaled Breath Bellarmino, Nicolo Bozzini, Giorgio Cantoro, Riccardo Castelletti, Francesco Castelluzzo, Michele Ciricugno, Carla Correale, Raffaele Gasperina, Daniela Dalla Dentali, Francesco Poggialini, Giovanni Salerno, Piergiorgio Squillero, Giovanni Taborelli, Stefano Machine Learning Quantitative Methods The SARS-CoV-2 coronavirus emerged in 2019, causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next four years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive detection system, which exploits only the exhaled breath. Specifically, provided an air sample, the mass spectra in the 10--351 mass-to-charge range are measured using an original nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on some 300 subjects who were concerned about being infected, either due to exhibiting symptoms or having quite recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 0.95 accuracy, 0.94 recall, 0.96 specificity, and 0.92 F1-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner. |
| title | COVID-19 Detection from Exhaled Breath |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2305.19211 |