Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons

• Published in: Diagnostics (Basel) Vol. 14; no. 20; p. 2273
• Main Authors: Carreiro-Martins, Pedro, Paixão, Paulo, Caires, Iolanda, Matias, Pedro, Gamboa, Hugo, Soares, Filipe, Gomez, Pedro, Sousa, Joana, Neuparth, Nuno
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2024; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Classification; Consent; COVID-19; Data collection; Datasets; diagnostic tests; Infections; Machine learning; Medical screening; Methods; Neural networks; Pandemics; Questionnaires; Respiratory organs; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Sounds; speech; Support vector machines; voice; Portugal; voice; SARS-CoV-2; diagnostic tests; machine learning; speech
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14202273

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.