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Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata

arXiv – CS AI|George P. Kafentzis, Stephane Tetsing, Joe Brew, Lola Jover, Mindaugas Galvosas, Carlos Chaccour, Peter M. Small|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed an AI system that can detect tuberculosis from cough recordings with 70% accuracy using audio alone, improving to 81% when combined with clinical metadata. The study used real-world data from a phone-based app across Africa and Asia, suggesting mobile applications could enhance TB diagnosis in community health settings.

Key Takeaways

→AI achieved 70% accuracy (AUC 0.70) in detecting tuberculosis from cough audio recordings alone
→Accuracy improved to 81% when combining cough analysis with demographic and clinical factors
→Study used large dataset from real-world phone app recordings across Africa and Asia without manual annotation
→Mobile phone-based TB screening could reduce costs and improve case-finding for community health workers
→Research demonstrates potential for automated respiratory disease diagnosis through audio analysis

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#ai-healthcare #machine-learning #medical-diagnosis #mobile-health #tuberculosis #audio-analysis #public-health #disease-detection

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