  Tuberculosis (TB) is an infectious disease caused by the bacterium
Mycobacterium tuberculosis and primarily affects the lungs, as well as other
body parts. TB is spread through the air when an infected person coughs,
sneezes, or talks. Medical doctors diagnose TB in patients via clinical
examinations and specialized tests. However, coughing is a common symptom of
respiratory diseases such as TB. Literature suggests that cough sounds coming
from different respiratory diseases can be distinguished by both medical
doctors and computer algorithms. Therefore, cough recordings associated with
patients with and without TB seems to be a reasonable avenue of investigation.
In this work, we utilize a very large dataset of TB and non-TB cough audio
recordings obtained from the south-east of Africa, India, and the south-east of
Asia using a fully automated phone-based application (Hyfe), without manual
annotation. We fit statistical classifiers based on spectral and time domain
features with and without clinical metadata. A stratified grouped
cross-validation approach shows that an average Area Under Curve (AUC) of
approximately 0.70 $\pm$ 0.05 both for a cough-level and a participant-level
classification can be achieved using cough sounds alone. The addition of
demographic and clinical factors increases performance, resulting in an average
AUC of approximately 0.81 $\pm$ 0.05. Our results suggest mobile phone-based
applications that integrate clinical symptoms and cough sound analysis could
help community health workers and, most importantly, health service programs to
improve TB case-finding efforts while reducing costs, which could substantially
improve public health.
