Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis

Background
Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. 
Methods and Findings
A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration...

背景
结核病（Tuberculosis）是全球十大死因之一，也是单一感染源导致死亡的首要原因。到2030年根除结核病疫情是联合国可持续发展的核心目标之一。早期诊断对于实现这一目标至关重要，因其可改善个体预后并降低无症状感染者的传播率。我们旨在通过开发基于机器学习算法的快速敏感诊断方法，减少对专家干预的需求，从而助力这一目标的实现。

方法与发现
本研究采用单分子荧光免疫吸附测定法（single-molecule fluorescence immunosorbent assay），从20份临床患者样本及一组加标人尿对照样本中检测结核病生物标志物脂阿拉伯甘露聚糖（lipoarabinomannan）。结核病状态通过GeneXpert MTB/RIF检测及细胞培养分别确认。研究开发了两种机器学习算法——自动模型与半自动模型，并利用校准后的脂阿拉伯甘露聚糖滴定数据进行训练...