1 - Comprehensive Health Research Center (CHRC), LA-REAL, NOVA Medical School
2 - Serviço de Imunoalergologia, Hospital de Dona Estefânia, ULS São José,
3 - Fraunhofer Portugal AICOS, AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
4 - Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys), Faculdade de Ciências e Tecnologia, NOVA University of Lisbon,
5 - NeuSpeLab, CTB, Universidad Politécnica de Madrid, Campus de Montegancedo,
6 - NOS Inovação, Rua Actor António Silva, 9–6◦ Piso, Campo Grande, 1600-404 Lisboa, Portugal
- Artigo Original publicado na revista Diagnostics (Basel). 2024 Oct 12;14(20):2273. doi: 10.3390/diagnostics14202273
Resumo:
Background/Objectives: The interest in processing human speech and other humangenerated 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% SARSCoV-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.
Palavras Chave: diagnostic tests; machine learning; SARS-CoV-2; speech; voice