Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of the leading infectious causes of mortality worldwide. To prevent TB from spreading within the body, which causes life-threatening complications, timely and effective anti-TB treatment is crucial. Cough, an objective biomarker for TB, is a triage tool that monitors treatment response and regresses with successful therapy. Current gold standards for TB diagnosis are slow or inaccessible, especially in rural areas where TB is most prevalent. In addition, current machine learning (ML) diagnosis research, like utilizing chest radiographs, is ineffective and does not monitor treatment progression. To enable effective diagnosis, an ensemble model was developed that analyzes, using a novel ML architecture, coughs' acoustic epidemiologies from smartphones' microphones to detect TB. The architecture includes a 2D-CNN and XGBoost that was trained on 724,964 cough audio samples and demographics from 7 countries. After feature extraction (Mel-spectrograms) and data augmentation (IR-convolution), the model achieved AUROC (area under the receiving operator characteristic) of 88%, surpassing WHO's requirements for screening tests. The results are available within 15 seconds and can easily be accessible via a mobile app. This research helps to improve TB diagnosis through a promising accurate, quick, and accessible triaging tool.

翻译：结核病（TB）是一种主要影响肺部的细菌性疾病，是全球主要的传染性死因之一。为防止结核病在体内扩散并引发危及生命的并发症，及时有效的抗结核治疗至关重要。咳嗽作为结核病的客观生物标志物，是一种分诊工具，可监测治疗反应，并在成功治疗后症状消退。当前结核病诊断的金标准方法要么速度缓慢，要么难以普及，尤其是在结核病高发的农村地区。此外，现有的机器学习（ML）诊断研究（如利用胸部X光片）效率低下，且无法监测治疗进展。为实现高效诊断，本文开发了一种集成模型，该模型采用新型ML架构，通过智能手机麦克风分析咳嗽声学流行病学特征以检测结核病。该架构包含2D-CNN和XGBoost，基于来自7个国家的724,964个咳嗽音频样本和人口统计数据训练而成。在特征提取（梅尔频谱图）和数据增强（脉冲响应卷积）后，模型AUROC（受试者工作特征曲线下面积）达到88%，超过了世界卫生组织对筛查测试的要求。结果可在15秒内获得，并可通过移动应用程序轻松访问。本研究通过提供一种准确、快速且可及的分诊工具，有助于改善结核病诊断。