A dataset of Solicited Cough Sound for Tuberculosis Triage Testing.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-18 DOI:10.1038/s41597-024-03972-z

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

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引用次数: 0

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.

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用于肺结核分诊测试的咳嗽声数据集。

咳嗽是肺部疾病的一种常见症状，也是常被忽视的症状。人们通常认为咳嗽难以量化，经常是自限性的，而且没有特异性。然而，咳嗽在许多肺部疾病的临床检测中起着核心作用，其中包括肺结核（TB），它仍然是全球头号传染病杀手。肺结核筛查目前依赖于自我报告的咳嗽，而这种咳嗽无法达到世界卫生组织（WHO）规定的肺结核分诊测试准确性目标。基于咳嗽声的人工智能（AI）模型已被开发用于多种呼吸系统疾病，但在肺结核方面所做的工作还很有限。为了支持开发准确的、基于咳嗽声的结核病护理点分诊工具，我们汇编了一个大型的多国数据库，其中包含了接受结核病评估的患者的咳嗽声。该数据集包括来自 2,143 名患者的 70 多万次咳嗽声，并附有详细的人口统计学、临床和微生物学诊断信息。我们的目标是增强研究人员开发咳声分析模型的能力，以改进结核病诊断，因为要结束这一长期存在的流行病，亟需创新方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.