A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases.

Autor: Golyak IS; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia., Anfimov DR; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia., Demkin PP; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia., Berezhanskiy PV; Sechenov First Moscow State Medical University, Moscow, Russia., Nebritova OA; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia., Morozov AN; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia., Fufurin IL; Department of Physics, Bauman Moscow State Technical University, Moscow, Russia.
Jazyk: angličtina
Zdroj: Journal of biophotonics [J Biophotonics] 2024 Oct; Vol. 17 (10), pp. e202400151. Date of Electronic Publication: 2024 Jul 29.
DOI: 10.1002/jbio.202400151
Abstrakt: Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm -1 ), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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