Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning

Viktor Dremin, Zbignevs Marcinkevics, Evgeny Zherebtsov, Alexey Popov, Andris Grabovskis, Hedviga Kronberga, Kristine Geldnere, Alexander Doronin, Igor Meglinski, Alexander Bykov

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number9316275
Pages (from-to)1207-1216
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number4
Early online date6 Jan 2021
Publication statusPublished - Apr 2021

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

Funding: Viktor Dremin kindly acknowledges personal support from the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie grant agreement No. 839888. The authors
acknowledge the support of the Academy of Finland (grants: 314369 –
RADDESS programme, 290596, and 318281) as well as INFOTECH
strategic funding. (


  • Hyperspectral imaging
  • diabetes mellitus
  • polarization
  • skin complications


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