Fast and automated biomarker detection in breath samples with machine learning

• URL: http://arxiv.org/abs/2006.01772v1
• Date: Sun, 24 May 2020 11:44:28 GMT
• Title: Fast and automated biomarker detection in breath samples with machine learning
• Authors: Angelika Skarysz, Dahlia Salman, Michael Eddleston, Martin Sykora, Eugenie Hunsicker, William H Nailon, Kareen Darnley, Duncan B McLaren, C L Paul Thomas and Andrea Soltoggio
• Abstract summary: Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis. We propose a system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs.
• Score: 1.2026897155625271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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