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COVID-19 Detection from Exhaled Breath

Nicolo Bellarmino, Giorgio Bozzini, Riccardo Cantoro, Francesco Castelletti, Michele Castelluzzo, Carla Ciricugno, Raffaele Correale, Daniela Dalla Gasperina, Francesco Dentali, Giovanni Poggialini, Piergiorgio Salerno, Giovanni Squillero, Stefano Taborelli

TL;DR

This paper introduces a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath and shows a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19.

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 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.

COVID-19 Detection from Exhaled Breath

TL;DR

This paper introduces a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath and shows a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19.

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 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.
Paper Structure (11 sections, 5 figures, 4 tables)

This paper contains 11 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Experimental Evaluation
  4. Results
  5. Discussion
  6. Acknowledgement

Figures (5)

  • Figure 1: Total Ion Current (TIC) of a recording from one sample. The recording is made up of about 10 acquisitions (green dots), each corresponding to a mass spectrum. The spectra used for the analysis are selected on the plateau of TIC (red dotted region).
  • Figure 2: Aligned and non-aligned peaks of the mass spectrum of a single patient.
  • Figure 3: Examples of whole spectra (10351m/z) for negative subjects (top, blue) and positive ones (bottom, red)
  • Figure 4: The comparison of spectra before (top) and after the filtering and normalizing procedure (bottom) shows the removal of low-frequency noise. Negative patients are on the left (blue), and positive on the right (red).
  • Figure 5: 2D t-SNE representation of the whole spectra for all the 47,084 samples generated.