| Title: | Fast and automated biomarker detection in breath samples with machine learning |
| Author(s): | Skarysz A; Salman D; Eddleston M; Sykora M; Hunsicker E; Nailon WH; Darnley K; McLaren DB; Thomas CLP; Soltoggio A; |
| Address: | "Computer Science Department, School of Science, Loughborough University, Loughborough, United Kingdom. Centre for Analytical Science, School of Science, Loughborough University, Loughborough, United Kingdom. Pharmacology, Toxicology & Therapeutics Unit, University of Edinburgh, Edinburgh, United Kingdom. Centre for Information Management, School of Business and Economics, Loughborough University, Loughborough, United Kingdom. Mathematical Sciences Department, School of Science, Loughborough University, Loughborough, United Kingdom. Edinburgh Cancer Centre, NHS Lothian, Edinburgh, United Kingdom. Clinical Research Facility, Western General Hospital, NHS Lothian, Edinburgh, United Kingdom" |
| DOI: | 10.1371/journal.pone.0265399 |
| ISSN/ISBN: | 1932-6203 (Electronic) 1932-6203 (Linking) |
| 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 machine learning-based 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. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods 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 novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency" |
| Keywords: | Biomarkers/analysis *Breath Tests/methods Gas Chromatography-Mass Spectrometry/methods Humans Machine Learning *Volatile Organic Compounds/analysis; |
| Notes: | "MedlineSkarysz, Angelika Salman, Dahlia Eddleston, Michael Sykora, Martin Hunsicker, Eugenie Nailon, William H Darnley, Kareen McLaren, Duncan B Thomas, C L Paul Soltoggio, Andrea eng Research Support, Non-U.S. Gov't 2022/04/13 PLoS One. 2022 Apr 12; 17(4):e0265399. doi: 10.1371/journal.pone.0265399. eCollection 2022" |
