Publication
Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.
| dc.contributor.author | Carreiro-Martins, Pedro | |
| dc.contributor.author | Paixão, Paulo | |
| dc.contributor.author | Caires, Iolanda | |
| dc.contributor.author | Matias, Pedro | |
| dc.contributor.author | Gamboa, Hugo | |
| dc.contributor.author | Soares, Filipe | |
| dc.contributor.author | Gomez, Pedro | |
| dc.contributor.author | Sousa, Joana | |
| dc.contributor.author | Neuparth, Nuno | |
| dc.date.accessioned | 2025-07-25T13:51:28Z | |
| dc.date.available | 2025-07-25T13:51:28Z | |
| dc.date.issued | 2024-10-12 | |
| dc.description.abstract | Background/Objectives: The interest in processing human speech and other human-generated audio signals as a diagnostic tool has increased due to the COVID-19 pandemic. The project OSCAR (vOice Screening of CoronA viRus) aimed to develop an algorithm to screen for COVID-19 using a dataset of Portuguese participants with voice recordings and clinical data. Methods: This cross-sectional study aimed to characterise the pattern of sounds produced by the vocal apparatus in patients with SARS-CoV-2 infection documented by a positive RT-PCR test, and to develop and validate a screening algorithm. In Phase II, the algorithm developed in Phase I was tested in a real-world setting. Results: In Phase I, after filtering, the training group consisted of 166 subjects who were effectively available to train the classification model (34.3% SARS-CoV-2 positive/65.7% SARS-CoV-2 negative). Phase II enrolled 58 participants (69.0% SARS-CoV-2 positive/31.0% SARS-CoV-2 negative). The final model achieved a sensitivity of 85%, a specificity of 88.9%, and an F1-score of 84.7%, suggesting voice screening algorithms as an attractive strategy for COVID-19 diagnosis. Conclusions: Our findings highlight the potential of a voice-based detection strategy as an alternative method for respiratory tract screening. | eng |
| dc.identifier.citation | Diagnostics (Basel) . 2024 Oct 12;14(20):2273 | |
| dc.identifier.doi | 10.3390/diagnostics14202273 | |
| dc.identifier.other | 39451596 | |
| dc.identifier.uri | http://hdl.handle.net/10400.17/5123 | |
| dc.language.iso | en | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | SARS-CoV-2 | |
| dc.subject | diagnostic tests | |
| dc.subject | machine learning | |
| dc.subject | speech | |
| dc.subject | voice | |
| dc.subject | HDE ALER | |
| dc.title | Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons. | |
| dc.type | text | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 20 | |
| oaire.citation.startPage | 2273 | |
| oaire.citation.volume | 14 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |