IEEE Transactions on Medical Imaging, volume 40, issue 4, pages 1207-1216
Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning
Dremin Viktor
1, 2
,
Marcinkevics Zbignevs
3
,
Popov Alexey
4
,
Grabovskis Andris
5
,
Kronberga Hedviga
6
,
Geldnere Kristine
7
,
Doronin Alexander
8
,
MEGLINSKI IGOR
1, 9, 10, 11
,
3
Department of Human and Animal Physiology, University of Latvia, Riga, Latvia
|
5
Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
|
6
Medical Center Plavnieki, Riga, Latvia
|
7
Pauls Stradins Clinical University Hospital, Riga, Latvia
|
8
Computer Graphics Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
|
Publication type: Journal Article
Publication date: 2021-04-01
Journal:
IEEE Transactions on Medical Imaging
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 10.6
ISSN: 02780062, 15580062
Computer Science Applications
Electrical and Electronic Engineering
Radiological and Ultrasound Technology
Software
Abstract
Aging and diabetes lead to protein glycation and cause dysfunction of collagen-containing tissues. The accompanying structural and functional changes of collagen significantly contribute to the development of various pathological malformations affecting the skin, blood vessels, and nerves, causing a number of complications, increasing disability risks and threat to life. In fact, no methods of non-invasive assessment of glycation and associated metabolic processes in biotissues or prediction of possible skin complications, e.g., ulcers, currently exist for endocrinologists and clinical diagnosis. In this publication, utilizing emerging photonics-based technology, innovative solutions in machine learning, and definitive physiological characteristics, we introduce a diagnostic approach capable of evaluating the skin complications of diabetes mellitus at the very earlier stage. The results of the feasibility studies, as well as the actual tests on patients with diabetes and healthy volunteers, clearly show the ability of the approach to differentiate diabetic and control groups. Furthermore, the developed in-house polarization-based hyperspectral imaging technique accomplished with the implementation of the artificial neural network provides new horizons in the study and diagnosis of age-related diseases.
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1 publication, 1.89%
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1 publication, 1.89%
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1 publication, 1.89%
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Citations by publishers
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9
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Springer Nature
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9 publications, 16.98%
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7 publications, 13.21%
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7 publications, 13.21%
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IEEE
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6 publications, 11.32%
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5 publications, 9.43%
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SPIE
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SPIE
4 publications, 7.55%
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Frontiers Media S.A.
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2 publications, 3.77%
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1 publication, 1.89%
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Taylor & Francis
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Taylor & Francis
1 publication, 1.89%
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Wiley
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Wiley
1 publication, 1.89%
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Hindawi Limited
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Hindawi Limited
1 publication, 1.89%
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Korean Academy of Medical Sciences
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Korean Academy of Medical Sciences, 1, 1.89%
Korean Academy of Medical Sciences
1 publication, 1.89%
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1
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Dremin V. et al. Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning // IEEE Transactions on Medical Imaging. 2021. Vol. 40. No. 4. pp. 1207-1216.
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Dremin V., Marcinkevics Z., Zherebtsov E., Popov A., Grabovskis A., Kronberga H., Geldnere K., Doronin A., MEGLINSKI I., Bykov A. Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning // IEEE Transactions on Medical Imaging. 2021. Vol. 40. No. 4. pp. 1207-1216.
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TY - JOUR
DO - 10.1109/TMI.2021.3049591
UR - https://doi.org/10.1109%2FTMI.2021.3049591
TI - Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning
T2 - IEEE Transactions on Medical Imaging
AU - Dremin, Viktor
AU - Marcinkevics, Zbignevs
AU - Zherebtsov, Evgeny
AU - Popov, Alexey
AU - Grabovskis, Andris
AU - Kronberga, Hedviga
AU - Geldnere, Kristine
AU - Doronin, Alexander
AU - MEGLINSKI, IGOR
AU - Bykov, Alexander
PY - 2021
DA - 2021/04/01 00:00:00
PB - IEEE
SP - 1207-1216
IS - 4
VL - 40
SN - 0278-0062
SN - 1558-0062
ER -
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@article{2021_Dremin,
author = {Viktor Dremin and Zbignevs Marcinkevics and Evgeny Zherebtsov and Alexey Popov and Andris Grabovskis and Hedviga Kronberga and Kristine Geldnere and Alexander Doronin and IGOR MEGLINSKI and Alexander Bykov},
title = {Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning},
journal = {IEEE Transactions on Medical Imaging},
year = {2021},
volume = {40},
publisher = {IEEE},
month = {apr},
url = {https://doi.org/10.1109%2FTMI.2021.3049591},
number = {4},
pages = {1207--1216},
doi = {10.1109/TMI.2021.3049591}
}
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MLA
Copy
Dremin, Viktor, et al. “Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning.” IEEE Transactions on Medical Imaging, vol. 40, no. 4, Apr. 2021, pp. 1207-1216. https://doi.org/10.1109%2FTMI.2021.3049591.