论文标题
通过机器学习，在呼吸样品中快速自动生物标志物检测
Fast and automated biomarker detection in breath samples with machine learning
论文作者
论文摘要
人类呼吸中的挥发性有机化合物（VOC）可以揭示大量的健康状况，可用于快速，准确和非侵入性诊断。气相色谱 - 质谱法（GC-MS）用于测量VOC，但其应用受到专家驱动的数据分析的限制，该数据分析是耗时，主观的，并且可能引入错误。我们提出了一个执行GC-MS数据分析的系统，该系统利用了深度学习模式识别能力，可以直接从原始数据中直接检测VOC，从而绕过专家主导的处理。新提出的方法表明，通过仅在一小部分时间内检测出明显更高的VOC数量，同时保持高特异性，这表明了表现优于专家主导的分析。这些结果表明，提出的方法可以通过降低时间和成本以及提高准确性和一致性来帮助大规模部署基于呼吸的诊断。
Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. The new proposed approach showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed method can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.
