  Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of
the leading infectious causes of mortality worldwide. To prevent TB from
spreading within the body, which causes life-threatening complications, timely
and effective anti-TB treatment is crucial. Cough, an objective biomarker for
TB, is a triage tool that monitors treatment response and regresses with
successful therapy. Current gold standards for TB diagnosis are slow or
inaccessible, especially in rural areas where TB is most prevalent. In
addition, current machine learning (ML) diagnosis research, like utilizing
chest radiographs, is ineffective and does not monitor treatment progression.
To enable effective diagnosis, an ensemble model was developed that analyzes,
using a novel ML architecture, coughs' acoustic epidemiologies from
smartphones' microphones to detect TB. The architecture includes a 2D-CNN and
XGBoost that was trained on 724,964 cough audio samples and demographics from 7
countries. After feature extraction (Mel-spectrograms) and data augmentation
(IR-convolution), the model achieved AUROC (area under the receiving operator
characteristic) of 88%, surpassing WHO's requirements for screening tests. The
results are available within 15 seconds and can easily be accessible via a
mobile app. This research helps to improve TB diagnosis through a promising
accurate, quick, and accessible triaging tool.
