A dataset of Solicited Cough Sound for Tuberculosis Triage Testing

Autor: Sophie Huddart, Vijay Yadav, Solveig K. Sieberts, Larson Omberg, Mihaja Raberahona, Rivo Rakotoarivelo, Issa N. Lyimo, Omar Lweno, Devasahayam J. Christopher, Nguyen Viet Nhung, Grant Theron, William Worodria, Charles Y. Yu, Christine M. Bachman, Stephen Burkot, Puneet Dewan, Sourabh Kulhare, Peter M. Small, Adithya Cattamanchi, Devan Jaganath, Simon Grandjean Lapierre
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Data, Vol 11, Iss 1, Pp 1-6 (2024)
Druh dokumentu: article
ISSN: 2052-4463
DOI: 10.1038/s41597-024-03972-z
Popis: Abstract Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.
Databáze: Directory of Open Access Journals