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Fast and Accurate Exhaled Breath Ammonia Measurement
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Fast and automated biomarker detection in breath samples with machine learning.

Angelika Skarysz1, Dahlia Salman2, Michael Eddleston3

  • 1Computer Science Department, School of Science, Loughborough University, Loughborough, United Kingdom.

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|April 12, 2022
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Summary

Machine learning analyzes volatile organic compounds (VOCs) in breath for faster, more accurate disease diagnosis. This AI approach automates gas chromatography-mass spectrometry (GC-MS) data analysis, improving upon traditional expert methods.

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Computational Biology

Background:

  • Volatile organic compounds (VOCs) in human breath serve as biomarkers for various health conditions.
  • Current diagnostic methods using VOCs, like gas chromatography-mass spectrometry (GC-MS), rely on time-consuming and subjective expert analysis.
  • Limitations of expert-driven GC-MS analysis include potential for errors and lack of scalability.

Purpose of the Study:

  • To develop and evaluate a machine learning-based system for automated GC-MS data analysis.
  • To bypass the need for expert-led processing in VOC detection from breath samples.
  • To improve the speed, accuracy, and consistency of breath-based diagnostics.

Main Methods:

  • A machine learning system leveraging deep learning pattern recognition was proposed.
  • Four types of convolutional neural networks (CNNs) were evaluated: VGG16, VGG-like, densely connected, and residual CNNs.
  • The system was trained and tested on clinical breath samples to detect VOCs directly from raw GC-MS data.

Main Results:

  • The machine learning approach significantly outperformed expert-led analysis in detecting VOCs.
  • The AI system identified a substantially higher number of VOCs compared to traditional methods.
  • The proposed methods achieved this with high specificity and in a fraction of the time.

Conclusions:

  • Machine learning, particularly deep learning CNNs, offers a powerful alternative for analyzing GC-MS data.
  • Automated VOC detection via AI can enhance the accuracy, speed, and consistency of breath diagnostics.
  • This approach holds promise for the widespread adoption of non-invasive, cost-effective breath-based disease screening.