University of California San Francisco, School of Medicine, 533 Parnassus Ave, San Francisco, CA, 94143, USA.
Sage Bionetworks, Seattle, WA, 98103, USA.
Sci Data. 2024 Oct 18;11(1):1149. doi: 10.1038/s41597-024-03972-z.
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.
咳嗽是肺部疾病的常见且常被忽视的症状。咳嗽通常被认为难以量化，经常是自限性的，且无特异性。然而，咳嗽在许多肺部疾病的临床检测中起着核心作用，包括结核病（TB），这仍然是全球领先的传染病杀手。目前，TB 筛查依赖于自我报告的咳嗽，但未能达到世界卫生组织（WHO）对 TB 分诊测试的准确性目标。已经为几种呼吸系统疾病开发了基于咳嗽声音的人工智能（AI）模型，但在 TB 方面的工作有限。为了支持开发一种准确的、基于咳嗽的 TB 即时护理分诊工具，我们从正在接受 TB 评估的个体中汇编了一个大型多国咳嗽声音数据库。该数据集包括来自 2143 名个体的超过 700,000 个咳嗽声音，这些个体具有详细的人口统计学、临床和微生物学诊断信息。我们的目标是为研究人员开发咳嗽声音分析模型提供支持，以改善 TB 诊断，在需要创新方法来终结这一长期流行疾病的情况下，这一点至关重要。
