Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

ABSTRACT: Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy i

SUBMITTER: Robotti C 

PROVIDER: S-EPMC8616736 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature