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Although at-home rapid antigen tests are currently available&#44; they are invasive and uncomfortable&#44; vary in accuracy depending on how they are done and must be quickly accessible&#46;<a class="elsevierStyleCrossRef" href="#bib0001"><span class="elsevierStyleSup">1</span></a> These characteristics may raise perceived barriers to COVID-19 testing and limit the detection of the disease&#46; Vaccines have been unevenly distributed and accepted&#44; and cases are still increasing in some areas&#46; It is unclear what the future holds for the pandemic&#44; but it will likely continue to require effort from all involved&#46;</p><p id="para0002" class="elsevierStylePara elsevierViewall">SARS-CoV-2 infection primarily affects the respiratory tract&#44; including the upper and lower airways&#44; and contributes to the disruption of normal vocalisations&#46; Given this&#44; some research groups have attempted to develop more convenient and accessible COVID-19 diagnostic methods by using machine learning to analyse voice recordings and other audio signals&#44; such as coughing&#44; breathing sounds&#44; and breathing rate&#46; Voice and cough analysis are an attractive approach to screening for respiratory disease symptoms<a class="elsevierStyleCrossRef" href="#bib0002"><span class="elsevierStyleSup">2</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0003"><span class="elsevierStyleSup">3</span></a> as sound recordings are simple to acquire and non-invasive&#46; However&#44; due to subtle differences in voice and cough characteristics&#44; artificial intelligence is required to detect specific disease patterns&#44; discard confounding factors that induce similar manifestations&#44; and reduce the effects of environmental noise&#46; Geographic and idiolectal linguistic variations can also affect analyses of these samples&#46;</p><p id="para0003" class="elsevierStylePara elsevierViewall">Most existing algorithms rely on crowdsourced audio sample databases&#46; However&#44; such databases do not ensure the quality of the recordings and contain only a limited number of positive cases&#46; Notable examples of respiratory audio&#8211;based sample collection include studies that used the Coswara dataset<a class="elsevierStyleCrossRef" href="#bib0004"><span class="elsevierStyleSup">4</span></a> and the COVID-19 Sounds database&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">5</span></a> The accuracy reached by these studies&#8217; algorithms is debatable as the results derive from unreliable data sources&#44; either due to unconfirmed COVID-19 status or an uncontrolled recording environment&#46;</p><p id="para0004" class="elsevierStylePara elsevierViewall">For this reason&#44; some authors have chosen to conduct their own recording protocols to ensure the reliability of their results&#46; With a sample of 70 SARS-CoV-2-positive patients and 70 healthy individuals&#44; Robotti et al&#46;<a class="elsevierStyleCrossRef" href="#bib0006"><span class="elsevierStyleSup">6</span></a> demonstrated that machine learning could accurately discriminate both groups &#40;accuracy&#44; 90&#46;24&#37;&#41;&#46; Shimon et al&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a> included 57 patients &#40;25 SARS-CoV-2-positive&#41;&#44; achieving an average accuracy of 80&#37;&#46; Pinkas et al&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> included recordings of 29 SARS-CoV-2-positive and 59 SARS-CoV-2-negative patients and achieved an accuracy of 79&#37;&#46;</p><p id="para0005" class="elsevierStylePara elsevierViewall">Matias et al&#46;<a class="elsevierStyleCrossRef" href="#bib0009"><span class="elsevierStyleSup">9</span></a> used crowdsourced databases combined with quality assessment algorithms of voice recordings to overcome the above mentioned issues and detect SARS-CoV-2 infection with a more reliable and less noisy dataset&#46; Such an approach reached accuracy values ranging from 75&#37; to 84&#37; on Coswara&#44; and from 67&#37; to 81&#37; on a sub-set of COVID-19 sounds dataset&#46;</p><p id="para0006" class="elsevierStylePara elsevierViewall">The available literature suggests that machine learning tools using voice recordings and other audio signals could provide a non-invasive&#44; low-cost way to screen and flag for SARS-CoV-2 infection&#44; the results of which could later be confirmed with a clinically validated test&#46; Such algorithms are powerful in their ability to automatically learn hidden patterns from data and decision rules&#46; Combined with signal processing and expertise in feature engineering&#44; they can form reliable tools to help track infection in a more decentralised way&#46; This is particularly important in diseases where infection isolation is critical&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">Nevertheless&#44; future studies should validate the robustness of these algorithms in real-world settings and assess the feasibility of adapting this approach to other areas&#44; such as other respiratory infections&#44; such as seasonal influenza&#46; Additionally&#44; machine learning tools could also be used to monitor other chronic respiratory illnesses including asthma&#44; COPD&#44; obstructive sleep apnoea&#44; and tuberculosis&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> In the future&#44; this strategy might contribute to more decentralised screening for respiratory diseases&#44; facilitating early diagnosis and monitoring disease progression&#44; potentially improving outcomes for patients with these conditions&#44; and reducing pressure on the healthcare system&#46;</p></span></span>"
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                          "autores" => array:6 [
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                            3 => "S Ramoji"
                            4 => "SR Chetupalli"
                            5 => "R Nirmala"
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                            4 => "A Grammenos"
                            5 => "J Han"
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                          "autores" => array:6 [
                            0 => "G Pinkas"
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                            5 => "V&#46; Aharonson"
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