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Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one ofthe leading infectious causes of mortality worldwide. To prevent TB fromspreading within the body, which causes life-threatening complications, timelyand effective anti-TB treatment is crucial. Cough, an objective biomarker forTB, is a triage tool that monitors treatment response and regresses withsuccessful therapy. Current gold standards for TB diagnosis are slow orinaccessible, especially in rural areas where TB is most prevalent. Inaddition, current machine learning (ML) diagnosis research, like utilizingchest 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 fromsmartphones' microphones to detect TB. The architecture includes a 2D-CNN andXGBoost that was trained on 724,964 cough audio samples and demographics from 7countries. After feature extraction (Mel-spectrograms) and data augmentation(IR-convolution), the model achieved AUROC (area under the receiving operatorcharacteristic) of 88%, surpassing WHO's requirements for screening tests. Theresults are available within 15 seconds and can easily be accessible via amobile app. This research helps to improve TB diagnosis through a promisingaccurate, quick, and accessible triaging tool.

Early Detection of Tuberculosis with Machine Learning Cough Audio Analysis: Towards More Accessible Global Triaging Usage