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Optics and Spectroscopy, 2020, Volume 128, Issue 6, Pages 805–810
DOI: https://doi.org/10.21883/OS.2020.06.49414.46-20 (Mi os403) 		 
This article is cited in 12 scientific papers (total in 12 papers)

Saratov Fall Meeting 19: 7th International Symposium ''Optics and Biophotonics'', 23d International School for Junior Scientists and Students on Optics, Laser Physics & Biophotonics and 4th School on Advanced Fluorescence Imaging Methods
Biophotonics

Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning

Yu. V. Kistenevab, A. V. Tetenevab, T. V. Sorokinab, A. I. Knyazkovaac, O. A. Zakharovaac, A. Cuissetd, V. L. Vakse, E. G. Domrachevae, M. B. Chernyaevae, V. A. Anferteve, E. S. Simab, I. Yu. Yaninaaf, V. V. Tuchinafg, A. V. Borisovab

a Tomsk State University
b Siberian State Medical University, Tomsk, Russia
c Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, Tomsk, Russia
d Université du Littoral Côte d'Opale, Dunkerque, France
e Institute for Physics of Microstructures, Russian Academy of Sciences, Nizhnii Novgorod
f Saratov State University
g St. Petersburg National Research University of Information Technologies, Mechanics and Optics
Full-text PDF (991 kB) Citations (12)
DOI: https://doi.org/10.21883/OS.2020.06.49414.46-20
Abstract: Results of studying the exhaled air of patients with diabetes mellitus in comparison with healthy volunteers with the use of broadband terahertz time-domain spectroscopy are presented. Typical spectral subranges in which absorption spectrum profiles of breath tests of the target and control group differ most significantly are revealed: 0.560, 0.738, 0.970, 1.070, 1.140, 1.180, and 1.400 THz. Using the principal component analysis, it is shown that the set of absorption coefficients in these regions allows one to reliably separate the target and control groups. The obtained results are compared with measurements of acetone vapors in the exhaled air of patients with diabetes mellitus and healthy volunteers.
Keywords: diabetes, expired air, terahertz spectroscopy, machine learning.
Funding agency Grant number
Russian Foundation for Basic Research 18-52-16025
17-00-00275
17-00-00272
17-00-00184
17-00-00186
This work was performed within the framework of the Basic Research Program for State Academies of Sciences for 2013–2020, direction III.23. The study was supported by the Russian Foundation for Basic Research, project nos. 18-52-16025 and 17-00-00275 (17-00-00272, 17-00-00184, and 17-00-00186).
Received: 10.12.2019
Revised: 07.02.2020
Accepted: 28.02.2020
English version:
Optics and Spectroscopy, 2020, Volume 128, Issue 6, Pages 809–814
DOI: https://doi.org/10.1134/S0030400X20060090
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: Yu. V. Kistenev, A. V. Teteneva, T. V. Sorokina, A. I. Knyazkova, O. A. Zakharova, A. Cuisset, V. L. Vaks, E. G. Domracheva, M. B. Chernyaeva, V. A. Anfertev, E. S. Sim, I. Yu. Yanina, V. V. Tuchin, A. V. Borisov, “Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning”, Optics and Spectroscopy, 128:6 (2020), 805–810; Optics and Spectroscopy, 128:6 (2020), 809–814
Citation in format AMSBIB
\Bibitem{KisTetSor20}
\by Yu.~V.~Kistenev, A.~V.~Teteneva, T.~V.~Sorokina, A.~I.~Knyazkova, O.~A.~Zakharova, A.~Cuisset, V.~L.~Vaks, E.~G.~Domracheva, M.~B.~Chernyaeva, V.~A.~Anfertev, E.~S.~Sim, I.~Yu.~Yanina, V.~V.~Tuchin, A.~V.~Borisov
\paper Diagnosis of diabetes based on analysis of exhaled air by terahertz spectroscopy and machine learning
\jour Optics and Spectroscopy
\yr 2020
\vol 128
\issue 6
\pages 805--810
\mathnet{http://mi.mathnet.ru/os403}
\crossref{https://doi.org/10.21883/OS.2020.06.49414.46-20}
\elib{https://elibrary.ru/item.asp?id=43808065}
\transl
\jour Optics and Spectroscopy
\yr 2020
\vol 128
\issue 6
\pages 809--814
\crossref{https://doi.org/10.1134/S0030400X20060090}
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    • This publication is cited in the following 12 articles:
    1. Xianhao Wu, Rui Tao, Zhiyan Sun, Tianyao Zhang, Xingyue Li, Yuan Yuan, Shaowen Zheng, Can Cao, Zhaohui Zhang, Xiaoyan Zhao, Pei Yang, “Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 316 (2024), 124351  crossref
    2. Nick Rothbart, Alexandra Glück, Heinz-Wilhelm Hübers, “Terahertz Gas Spectroscopy Applied to Medicine and Metrology”, IEEE Trans. THz Sci. Technol., 14:5 (2024), 613  crossref
    3. V. N. Simonov, A. A. Fomkin, A. V. Shkolin, I. E. Menschikov, “Atseton-neitralnyi adsorbtsionnyi sensor vlazhnosti vydykhaemogo vozdukha pri diagnostike sakharnogo diabeta”, Fizikokhimiya poverkhnosti i zaschita materialov, 59:4 (2023), 456  crossref
    4. Olga Cherkasova, Denis Vrazhnov, Anastasia Knyazkova, Maria Konnikova, Evgeny Stupak, Vadim Glotov, Vyacheslav Stupak, Nazar Nikolaev, Andrey Paulish, Yan Peng, Yury Kistenev, Alexander Shkurinov, “Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment”, Applied Sciences, 13:9 (2023), 5434  crossref
    5. V. N. Simonov, A. A. Fomkin, A. V. Shkolin, I. E. Menshikov, “An Acetone-Neutral Adsorption-Based Sensor of Exhaled-Air Humidity for Diagnosis of Diabetes Mellitus”, Prot Met Phys Chem Surf, 59:4 (2023), 796  crossref
    6. V.V. Prischepa, V.E. Skiba, D.A. Vrazhnov, Yu.V. Kistenev, “Gas mixtures IR absorption spectra decomposition using a deep neural network”, Journal of Quantitative Spectroscopy and Radiative Transfer, 301 (2023), 108521  crossref
    7. Yury V. Kistenev, Alexey V. Borisov, Vyacheslav S. Zasedatel, Liudmila V. Spirina, “Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy”, Journal of Biophotonics, 16:12 (2023)  crossref
    8. Xuequan Chen, Hannah Lindley-Hatcher, Rayko I. Stantchev, Jiarui Wang, Kaidi Li, Arturo Hernandez Serrano, Zachary D. Taylor, Enrique Castro-Camus, Emma Pickwell-MacPherson, “Terahertz (THz) biophotonics technology: Instrumentation, techniques, and biomedical applications”, Chemical Physics Reviews, 3:1 (2022)  crossref
    9. Olga Cherkasova, Maria Konnikova, Yury Kistenev, Vladimir Vaks, Jean-Louis Coutaz, Alexander Shkurinov, Molecular and Laser Spectroscopy, 2022, 433  crossref
    10. Hui Yan, Wenhui Fan, Xu Chen, Hanqi Wang, Chong Qin, Xiaoqiang Jiang, “Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 271 (2022), 120908  crossref
    11. Ruichan Lv, Zhan Wang, Yaqun Ma, Wenjing Li, Jie Tian, “Machine Learning Enhanced Optical Spectroscopy for Disease Detection”, J. Phys. Chem. Lett., 13:39 (2022), 9238  crossref
    12. Hochong Park, Joo-Hiuk Son, “Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy”, Sensors, 21:4 (2021), 1186  crossref
    Citing articles in Google Scholar: Russian citations, English citations
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