Automated analysis of retinal microcirculation in type 1 diabetes mellitus
https://doi.org/10.14341/DM12931
Abstract
BACKGROUND: The paper is dedicated to the assessment of the retinal microvasculature in patients with type 1 diabetes mellitus (DM) with various features of the clinical course and different stages of diabetic retinopathy (DR). Automatic analysis of optical coherence tomogram angiograms (OCT-A) was carried out with specially developed software that provides the ability to estimate quantitative vascular parameters.
AIM: The purpose of the study was to assess diagnostic accuracy of clinical parameters and imaging biomarkers in type 1 diabetes using a new algorithm for OCT-A analysis.
MATERIALS AND METHODS: The study involved 186 people (365 eyes) with type 1 diabetes. The analysis of the OCT-A parameters was performed with a specially developed software. The range of studied parameters included: foveal avascular zone (FAZ), vessel area density (VAD), skeletonized vessel density (VSD), vessel diameter index (VDI), vascular curvature index (VCI) at the level of superficial (SCP) and deep (DCP) retinal capillary plexuses in the macular region. A correlation between the involvement of OCT-A biomarkers and age, degree of DM, increased glycated hemoglobin (HbA1c) level, stage of DR, and maximally corrected visual acuity (BCVA) was analysed.
RESULTS: A significant dependence of all quantitative OCT-A parameters on the age of and duration of diabetes (p<0.05) was revealed. An increase in FAZ SCP (K=0.788, p=0) and DCP (K=0.764, p=0.03); decrease in VAD SCP (K=-0.476, p=0) and DCP (K=-0.485, p=0); VSD SCP (K=0.692, p=0) and DCP (K=0.713, p=0); an increase in VDI SCP (K=0.698, p=0) and DCP (K=787, p<0.01), as well as an increase in the VCI SCP (K=0.735, p=0) and DCP (K=0.694, p p=0). An inverse relationship was found between HbA1c level and VAD SCP (K=-0.636, p=0) and DCP (K=-0.619, p=0.05) were identified as well as a direct relationship with VDI DCP (K=0.717, p<0.05). The influence of the HbA1c level on other parameters was not confirmed (p>0.05). The presence of correlation between BCVA and FAZ DCP (K=-0.728, p=0), as well as VSD DCP (K=-0.754, p=0) was proved.
CONCLUSION: As a result of a comprehensive analysis of clinical data and imaging biomarkers, a number of patterns that have diagnostic value in diabetic retinopathy were identified.
About the Authors
Yu. N. YusefRussian Federation
Yusef N. Yusef - MD, PhD.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
M. H. Durzhinskaya
Russian Federation
Madina H. Durzhinskaya - PhD; ResearcherID: D-3729-2018; Scopus Author ID: 57218617872.
11A Rossolimo street, 119021 Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
V. G. Pavlov
Russian Federation
Vladislav G. Pavlov.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
D. V. Petrachkov
Russian Federation
Denis V. Petrachkov - PhD.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
I. B. Gurevich
Russian Federation
Igor B. Gurevich - PhD in Physics and Mathematics.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
V. V. Yashina
Russian Federation
Vera V. Yashina - PhD in Physics and Mathematics.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
A. T. Tleubaev
Russian Federation
Adil T. Tleubaev - master.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
V. V. Fadeyev
Russian Federation
Valentin V. Fadeyev - MD, PhD, Зrofessor.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
I. V. Poluboyarinova
Russian Federation
Irina V. Poluboyarinova - PhD.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
A. E. Goldsmid
Russian Federation
Anna E. Goldsmid
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
R. А. Karamullina
Russian Federation
Regina A. Karamullina
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
D. V. Lipatov
Russian Federation
Dmitry V. Lipatov - MD, PhD, Professor.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
M. V. Budzinskaya
Russian Federation
Maria V. Budzinskaya - MD, PhD.
Moscow
Competing Interests:
Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи
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1. Figure 1. Algorithmic scheme for the formation of features with subsequent quantitative analysis of optical coherence tomograms-angiograms. | |
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For citations:
Yusef Yu.N., Durzhinskaya M.H., Pavlov V.G., Petrachkov D.V., Gurevich I.B., Yashina V.V., Tleubaev A.T., Fadeyev V.V., Poluboyarinova I.V., Goldsmid A.E., Karamullina R.А., Lipatov D.V., Budzinskaya M.V. Automated analysis of retinal microcirculation in type 1 diabetes mellitus. Diabetes mellitus. 2024;27(1):41-49. (In Russ.) https://doi.org/10.14341/DM12931

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