Capillaroscopic diagnostics of diabetic microangiopathy using artificial neural networks in patients with diabetes mellitus

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Background. The widespread prevalence of diabetes and the progressive deterioration of health against the background of this disease substantiate the need for the use of new methods for the early diagnosis of diabetic microangiopathies. The combined use of digital capillaroscopy and deep learning technologies will significantly improve the quality and speed of microvascular disorders diagnostics.

Aim. Development of a system for assessing capillary images based on artificial neural networks, as well as its testing for the early diagnosis of microangiopathy in patients with diabetes mellitus.

Material and methods. 136 patients (59 females and 77 males) with type 1 diabetes mellitus aged 25.82±6.05 years were examined and divided into two groups: the first group included 65 (47.8%) patients who had no symptoms of diabetic microangiopathies, the second group — 71 (52.2%) patients who were diagnosed with one or more diabetic microangiopathies. The control group consisted of 30 practically healthy volunteers. All patients with diabetes melitus, as well as individuals from the control group, underwent сapillaroscopic examination. The resulting images were analyzed using the developed evaluation system based on artificial neural networks. Statistical data processing was performed using the Student and Mann–Whitney tests (U-test), logistic regression analysis, and ROC analysis.

Results. In patients with diabetes mellitus, there was a decrease in the capillary network density in both groups and the diameter of the arterial sections in the second group. Capillaroscopy using the developed system showed a sufficient level of significance (χ2=21, p=0.000), high sensitivity (71.43%) and specificity (85.71%). This method can be used in the diagnosis of microangiopathy in diabetic patients.

Conclusion. The combined use of capillaroscopy and neural networks allows to increase the speed and quality of the examination, as well as simplify the interpretation of the resulting images.

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About the authors

Evgeniy S. Krutikov

Crimean Federal University named after V.I. Vernadsky

ORCID iD: 0000-0002-5754-4418
SPIN-code: 5967-2847

M.D., D. Sci. (Med.), Prof., Head of Depart., Depart. of Propaedeutics of Internal Medicine, S.I. Georgievsky Medical Academy

Russian Federation, Simferopol, Russia

Viktoriya A. Zhitova

Crimean Federal University named after V.I. Vernadsky

Author for correspondence.
ORCID iD: 0000-0002-1772-6399
SPIN-code: 3406-6010

M.D., Cand. Sci. (Med.), Assistant, Depart. of Basic and Clinical Pharmacology, S.I. Georgievsky Medical Academy

Russian Federation, Simferopol, Russia

Marina A. Rudenko

Crimean Federal University named after V.I. Vernadsky

ORCID iD: 0000-0002-8334-8453
SPIN-code: 1900-7487

Cand. Sci. (Technic.), Assoc. Prof., Depart. of Computer Engineering and Modeling

Russian Federation, Simferopol, Russia

Rustam O. Akaev

Crimean Federal University named after V.I. Vernadsky

ORCID iD: 0000-0002-3897-8042
SPIN-code: 9564-4620

M.D., Depart. of Propaedeutics of Internal Medicine, S.I. Georgievsky Medical Academy

Russian Federation, Simferopol, Russia

Daniil V. Burdin

Crimean Federal University named after V.I. Vernadsky

ORCID iD: 0000-0002-2817-1480
SPIN-code: 8608-6393

Junior Researcher, Depart. of Computer Engineering and Modeling

Russian Federation, Simferopol, Russia


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Supplementary files

Supplementary Files
1. Рис. 1. Результаты работы системы оценки капилляроскопических изображений в норме и при наличии ­микроангиопатии

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2. Рис. 2. ROC-кривая соотношения чувствительность/специфичность для системы оценки риска развития микроангиопатии

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