Author(s): Carreiro Martins, Pedro ; Paixão, Paulo ; Caires, Iolanda ; Matias, Pedro ; Gamboa, Hugo ; Soares, Filipe ; Gomez, Pedro ; Sousa, Joana ; Neuparth, Nuno
Date: 2024
Persistent ID: http://hdl.handle.net/10362/175174
Origin: Repositório Institucional da UNL
Subject(s): diagnostic tests; machine learning; SARS-CoV-2; speech; voice; Clinical Biochemistry
Funding Information: The present publication was funded by Funda\u00E7\u00E3o Ci\u00EAncia e Tecnologia, IP national support through CHRC (UIDP/04923/2020). The project \u201COSCAR\u2014vOice Screening of CoronA viRus\u201D (POCI-01-02B7-FEDER-051277) leading to this work is co-financed by ERDF-European Regional Fund through the Operational Program for Competitiveness and Internationalization, and by LISBOA 2020\u2014Regional Operational Program for Lisbon and Vale do Tejo. Publisher Copyright: © 2024 by the authors.
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.
