Using neural networks for non-invasive determination of glycated hemoglobin levels, illustrated by the application of an innovative portable glucometer in clinical practice
- Authors: Poliker E.E.1, Koshechkin K.A.1, Timokhin A.M.1, Klyukina E.V.1, Belyakova E.D.2, Brovko A.M.3, Lalayan A.S.4, Ermolaeva A.S.1
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Affiliations:
- The First Sechenov Moscow State Medical University
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
- Moscow Institute of Physics and Technology
- Lomonosov Moscow State University
- Issue: Vol 5, No 1S (2024)
- Pages: 124-126
- Section: Articles by YOUNG SCIENTISTS
- Submitted: 17.02.2024
- Accepted: 27.03.2024
- Published: 03.07.2024
- URL: https://jdigitaldiagnostics.com/DD/article/view/627099
- DOI: https://doi.org/10.17816/DD627099
- ID: 627099
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Abstract
BACKGROUND: In the last decade, there has been a significant increase in interest in non-invasive monitoring of blood glucose levels [1]. This is driven by the desire to reduce patient discomfort, as well as the risk of infections associated with traditional invasive methods [2]. Raman spectroscopy, considered as a promising approach for non-invasive measurements [3], combined with machine learning, has the potential to lead to more accurate and faster diagnostic methods for conditions related to glucose imbalances [4].
AIMS: Development and validation of a new portable glucometer based on Raman spectroscopy using machine learning methods for non-invasive determination of glycated hemoglobin (HbA1c) levels.
MATERIALS AND METHODS: The study was conducted on a sample of 100 volunteers of different age groups and genders, with varying health statuses, including individuals with type 1 and type 2 diabetes and those without diabetes. To collect data, we used a portable device developed by us, based on the registration of Raman spectra with laser excitation at 638 nm. The data were analyzed using Support Vector Machine neural networks.
RESULTS: After processing the spectroscopic measurements using Support Vector Machine, the system showed sensitivity (95,7%) and specificity (84,2%) in determining HbA1c levels comparable to traditional methods such as high-performance liquid chromatography. It was found that the algorithm is sufficiently adaptive and can be used across a wide range of skin types, regardless of the age and gender of the participants. The results suggest the possibility of using the developed device in clinical practice.
CONCLUSION: The developed portable glucometer based on Raman spectroscopy combined with machine learning algorithms could be a promising step towards non-invasive and continuous monitoring of glycemic levels in patients with diabetes.
Full Text
BACKGROUND: In the last decade, there has been a significant increase in interest in non-invasive monitoring of blood glucose levels [1]. This is driven by the desire to reduce patient discomfort, as well as the risk of infections associated with traditional invasive methods [2]. Raman spectroscopy, considered as a promising approach for non-invasive measurements [3], combined with machine learning, has the potential to lead to more accurate and faster diagnostic methods for conditions related to glucose imbalances [4].
AIMS: Development and validation of a new portable glucometer based on Raman spectroscopy using machine learning methods for non-invasive determination of glycated hemoglobin (HbA1c) levels.
MATERIALS AND METHODS: The study was conducted on a sample of 100 volunteers of different age groups and genders, with varying health statuses, including individuals with type 1 and type 2 diabetes and those without diabetes. To collect data, we used a portable device developed by us, based on the registration of Raman spectra with laser excitation at 638 nm. The data were analyzed using Support Vector Machine neural networks.
RESULTS: After processing the spectroscopic measurements using Support Vector Machine, the system showed sensitivity (95,7%) and specificity (84,2%) in determining HbA1c levels comparable to traditional methods such as high-performance liquid chromatography. It was found that the algorithm is sufficiently adaptive and can be used across a wide range of skin types, regardless of the age and gender of the participants. The results suggest the possibility of using the developed device in clinical practice.
CONCLUSION: The developed portable glucometer based on Raman spectroscopy combined with machine learning algorithms could be a promising step towards non-invasive and continuous monitoring of glycemic levels in patients with diabetes.
About the authors
Ekaterina E. Poliker
The First Sechenov Moscow State Medical University
Author for correspondence.
Email: katepoliker@gmail.com
ORCID iD: 0000-0002-9610-4511
SPIN-code: 3735-2532
Russian Federation, Moscow
Konstantin A. Koshechkin
The First Sechenov Moscow State Medical University
Email: koshechkin_k_a@staff.sechenov.ru
Russian Federation, Moscow
Alexander M. Timokhin
The First Sechenov Moscow State Medical University
Email: data.sup@ya.ru
Russian Federation, Moscow
Ekaterina V. Klyukina
The First Sechenov Moscow State Medical University
Email: katerina-klyukina@mail.ru
Russian Federation, Moscow
Ekaterina D. Belyakova
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
Email: belyakova_e_d@student.sechenov.ru
Russian Federation, Moscow
Artem M. Brovko
Moscow Institute of Physics and Technology
Email: ambrovko@mail.ru
Russian Federation, Moscow
Alina S. Lalayan
Lomonosov Moscow State University
Email: hemotech.ai@mail.ru
Russian Federation, Moscow
Alexandra S. Ermolaeva
The First Sechenov Moscow State Medical University
Email: a.s.arkhipova@inbox.ru
Russian Federation, Moscow
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