  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.
