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Zoi Kapsala, Aristofanis Pallikaris, Vasileia Maniadi, Vassiliki Louvari, Dimitrios Mamoulakis, Miltiadis Tsilimbaris; Quantitative Evaluation of Perifoveal Capillary Network in Young Diabetes Mellitus Type I Patients. Invest. Ophthalmol. Vis. Sci. 2013;54(15):195.
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© ARVO (1962-2015); The Authors (2016-present)
To develop an algorithm for the quantitative assessment of the retinal capillary microcirculation in diabetes mellitus type I (DM I) patients.
Thirty-two images of 32 eyes (16 eyes of 9 DM I patients and 16 eyes of 10 non DM patients) were chosen from the University Hospital of Heraklion digital fluorescein angiography database. The age was 18±5 years for the patient group (range: 12-26 years) and 17±7 years (range: 6-26 years) for the control group. For each eye a high resolution image was chosen and underwent a processing procedure using a commercial software (MatLab R2011a; The MathWorks Inc.). The so far developed algorithm traces both manually (by choosing with the cursor) and automatically the perifoveal capillary network in a subimage of field 20o*20o of the original one and provides measurements of the foveal avascular zone (FAZ) surface, the capillary density and the branch point density in the mentioned area.
The capillary mapping revealed a FAZ area of 0.23±0.06 degrees2 in the DM I group versus 0.22±0.05 degrees2 in the control group. The capillary density (capillary length in degrees/total area in square degrees) was 2.48±0.55 degrees-1 and 2.76±0.21 degrees-1 in each group, respectively. The last metric estimated, the branch point density, was 2.86±0.73 branch points/degrees2 in the diabetic group and 3.14±0.7 branch points/degrees2 in the control group. It seems that there is a slight reduction of these indexes in DM I patients when comparing with controls but none of these differences were statistically significant.
This approach constitutes our first attempt in order to develop an algorithm for the quantification of retinal microvessel alterations in young DM I patients. Further improvement of the algorithm will help us optimize the detection module and develop automated metrics for the quantification of diabetic retinal microangiopathy.
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