{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T23:26:37Z","timestamp":1790033197544,"version":"4.0.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000156","name":"Fonds de Recherche du Qu\u00e9bec - Sant\u00e9","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1038\/s41597-024-03972-z","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T06:02:17Z","timestamp":1729231337000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A dataset of Solicited Cough Sound for Tuberculosis Triage Testing"],"prefix":"10.1038","volume":"11","author":[{"given":"Sophie","family":"Huddart","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1033-0954","authenticated-orcid":false,"given":"Solveig K.","family":"Sieberts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larson","family":"Omberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihaja","family":"Raberahona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rivo","family":"Rakotoarivelo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Issa N.","family":"Lyimo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Lweno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devasahayam J.","family":"Christopher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen Viet","family":"Nhung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9216-2415","authenticated-orcid":false,"given":"Grant","family":"Theron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Worodria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles Y.","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine M.","family":"Bachman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Burkot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Dewan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sourabh","family":"Kulhare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter M.","family":"Small","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adithya","family":"Cattamanchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devan","family":"Jaganath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-1573","authenticated-orcid":false,"given":"Simon","family":"Grandjean Lapierre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"3972_CR1","unstructured":"WHO. Global TB report 2023 (2023)."},{"key":"3972_CR2","doi-asserted-by":"publisher","first-page":"e547","DOI":"10.1016\/S1473-3099(23)00375-4","volume":"23","author":"L Faust","year":"2023","unstructured":"Faust, L. et al. Improving measurement of tuberculosis care cascades to enhance people-centred care. Lancet Infect Dis 23, e547\u2013e557, https:\/\/doi.org\/10.1016\/S1473-3099(23)00375-4 (2023).","journal-title":"Lancet Infect Dis"},{"key":"3972_CR3","doi-asserted-by":"publisher","first-page":"e806","DOI":"10.1016\/S2214-109X(16)30199-1","volume":"4","author":"R Houben","year":"2016","unstructured":"Houben, R. et al. Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models. Lancet Glob Health 4, e806\u2013e815, https:\/\/doi.org\/10.1016\/S2214-109X(16)30199-1 (2016).","journal-title":"Lancet Glob Health"},{"key":"3972_CR4","unstructured":"WHO. The end TB strategy (2015)."},{"key":"3972_CR5","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/s43856-022-00149-w","volume":"2","author":"AJ Zimmer","year":"2022","unstructured":"Zimmer, A. J. et al. Making cough count in tuberculosis care. Commun Med (Lond) 2, 83, https:\/\/doi.org\/10.1038\/s43856-022-00149-w (2022).","journal-title":"Commun Med (Lond)"},{"key":"3972_CR6","unstructured":"WHO. High-priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting (2014)."},{"key":"3972_CR7","unstructured":"WHO. WHO operational handbook on tuberculosis. Module 2: screening - systematic screening for tuberculosis disease (2021)."},{"key":"3972_CR8","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1093\/cid\/ciab639","volume":"74","author":"G Tavaziva","year":"2022","unstructured":"Tavaziva, G. et al. Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy. Clin Infect Dis 74, 1390\u20131400, https:\/\/doi.org\/10.1093\/cid\/ciab639 (2022).","journal-title":"Clin Infect Dis"},{"key":"3972_CR9","doi-asserted-by":"publisher","unstructured":"Pahar, M. et al. Automatic cough classification for tuberculosis screening in a real-world environment. Physiol Meas 42, https:\/\/doi.org\/10.1088\/1361-6579\/ac2fb8 (2021).","DOI":"10.1088\/1361-6579\/ac2fb8"},{"key":"3972_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-31772-9","volume":"13","author":"GD Yellapu","year":"2023","unstructured":"Yellapu, G. D. et al. Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB. 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