COVID-19 detection from exhaled breath

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 4 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 COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and 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 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having 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, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-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.

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Publication Details

Journal
Scientific Reports
Published
2024-10-06
DOI
https://doi.org/10.1038/s41598-024-74104-1
Citations
8
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
3.23
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article

COVID-19 detection from exhaled breath

Carla Ciricugno, Giovanni Squillero, Francesco Dentali, Nicolò Bellarmino et al.
8 citations
Scientific Reports
COVID-19 diagnosis using AI
3.23
article

COVID-19 detection from exhaled breath

Carla Ciricugno, Giovanni Squillero, Francesco Dentali, Nicolò Bellarmino, Daniela Dalla Gasperina, Giovanni Poggialini, S Taborelli, Raffaele Correale, Giorgio Bozzini, Michele Castelluzzo, Riccardo Cantoro, Francesco Castelletti, Piergiorgio Salerno
article en
8 citations

Abstract

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 4 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 COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and 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 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having 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, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-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.

Scientific ReportsVol. 14(1)
University of Insubria (IT), Politecnico di Torino (IT), Azienda Socio Sanitaria Territoriale Lariana (IT)
Openalex Percentile: Top 8%
COVID-19 diagnosis using AI
3.23
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