Frequency comb and machine learning-based breath analysis for COVID-19 classification. (arXiv:2202.02321v1 [])

Human breath contains hundreds of volatile molecules that can provide
powerful, non-intrusive spectral diagnosis of a diverse set of diseases and
physiological/metabolic states. To unleash this tremendous potential for
medical science, we present a robust analytical method that simultaneously
measures tens of thousands of spectral features in each breath sample, followed
by efficient and detail-specific multivariate data analysis for unambiguous
binary medical response classification. We combine mid-infrared cavity-enhanced
direct frequency comb spectroscopy (CE-DFCS), capable of real-time collection
of tens of thousands of distinct molecular features at parts-per-trillion
sensitivity, with supervised machine learning, capable of analysis and
verification of extremely high-dimensional input data channels. Here, we
present the first application of this method to the breath detection of
Coronavirus Disease 2019 (COVID-19). Using 170 individual samples at the
University of Colorado, we report a cross-validated area under the
Receiver-Operating-Characteristics curve of 0.849(4), providing excellent
prediction performance. Further, this method detected a significant difference
between male and female breath as well as other variables such as smoking and
abdominal pain. Together, these highlight the utility of CE-DFCS for rapid,
non-invasive detection of diverse biological conditions and disease states. The
unique properties of frequency comb spectroscopy thus help establish precise
digital spectral fingerprints for building accurate databases and provide means
for simultaneous multi-response classifications. The predictive power can be
further enhanced with readily scalable comb spectral coverage.



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