TB-ACOUSTIC Africa is a machine learning–based tuberculosis screening system that uses cough and respiratory sound analysis to support non-sputum tuberculosis screening in low-resource settings. The system processes audio recordings, extracts acoustic features such as Mel-frequency cepstral coefficients (MFCC), spectral features, zero-crossing rate, and temporal features, and uses machine learning models to detect cough events and classify respiratory sounds. The system is designed as a low-cost, deployable screening and decision-support tool that can be integrated into mobile health applications and community health screening programs to support early tuberculosis screening and referral in settings with limited laboratory and radiographic infrastructure.
Agumba et al. (Sun,) studied this question.