Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection. Human breath has been evaluated in the setting of active tuberculosis using thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology. From the entire spectrum of volatile metabolites in breath, three random forest machine learning models were applied leading to the generation of a panel of 46 breath features. The twenty-two common features within each random forest model used were selected as a set that could distinguish subjects with confirmed pulmonary M. tuberculosis infection and people with other pathologies than TB.

中文翻译：

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通过多维气相色谱法对人类呼出气进行结核病诊断的初步调查-飞行时间质谱和机器学习。

结核病（TB）仍然是全球公共卫生疾病，每年夺走近180万人的生命。结核病的诊断可能是结核病控制最具挑战性的方面之一。诊断活动性结核病（培养和核酸扩增）的金标准取决于痰液，但是，在多达三分之一的结核病病例中，尚无法获得足够的生物学痰液样本。呼气分析可以替代痰液依赖性检查，它有可能提供一种简单，快速，无创且易于使用的诊断服务，可以对结核病的检测产生积极的影响。已使用热脱附全面二维气相色谱-飞行时间质谱方法在活动性结核病环境中评估了人的呼吸。从呼吸中的挥发性代谢物的整个谱图中，应用了三个随机森林机器学习模型，从而生成了包含46种呼吸特征的面板。选择每个所用随机森林模型中的22个共同特征作为一组特征，以区分确诊为肺结核分枝杆菌感染的受试者和结核以外的其他病理人群。