Purpose: In this study, it is shown that hemodynamic features are applicable as biomarkers to evaluate the progression of diabetic retinopathy (DR). Methods: Ninety-six fundus images from twenty-four subjects were selected. For each patient, four photographs were captured during the three years before DR and in the first year of DR. The vascular trees, which consisted of a parent vessel and two child branches were extracted, and at the branching nodes, the fluid dynamic conditions were estimated. Results: Veins were mostly affected during the last stage of diabetes before DR. In the arteries, the blood flow in both child branches and the Reynolds number in the smaller child branch were mostly affected. Conclusion: This study showed that hemodynamic features can add further information to the study of the progression of DR.
Donnelly, Richard and Horton, Edward. Vascular complications of diabetes: current issues in pathogenesis and treatment. John Wiley & Sons, 2008
World Health Organization. Global report on diabetes. World Health Organization; 2016.
Kifley, Annette andWang, Jie Jin and Cugati, Sudha and Wong, Tien Y and Mitchell, Paul. Retinal vascular caliber, diabetes, and retinopathy. American journal of ophthalmology, 2007;143(6): 1024–1026.
Klein, Ronald and Klein, Barbara EK and Moss, Scot E and Davis, Matthew D and DeMets, David L. The Wisconsin Epidemiologic Study of Diabetic Retinopathy: III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Archives of ophthalmology, 1984;102(4): 527–532.
Yang, Xiufen and Deng, Yu and Gu, Hong and Ren, Xuetao and Lim, Apiradee and Snellingen, Torkel and Liu, Xipu and Wang, Ningli and Pak, Jeong Won and Liu, Ningpu and others. Relationship of retinal vascular calibre and diabetic retinopathy in Chinese patients with type 2 diabetes mellitus: the Desheng Diabetic Eye Study. British Journal of Ophthalmology, 2016;
Leontidis, Georgios and Al-Diri, Bashir and Wigdahl, Jerey and Hunter, Andrew. Evaluation of geometric features as biomarkers of diabetic retinopathy for characterizing the retinal vascular changes during the progression of diabetes. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 2015; 5255–5259.
Leontidis, Georgios and Al-Diri, Bashir and Hunter, Andrew. Summarising the retinal vascular calibres in healthy, diabetic and diabetic retinopathy eyes. Computers in biology and medicine, 2016;72, 65–74.
Pournaras, Constantin J and Rungger-Brändle, Elisabeth and Riva, Charles E and Hardarson, Sveinn H and Stefansson, Einar. Regulation of retinal blood flow in health and disease. Progress in retinal and eye research, 2008;27(3): 284–330.
Harris, Alon. Atlas of ocular blood flow: vascular anatomy, pathophysiology, and metabolism. Butterworth-Heinemann Medical, 2003;
Vilser,Walthard and Nagel, Edgar and Lanzl, Ines. Retinal vessel analysis-new possibilities. Biomedizinische Technik/Biomedical Engineering, 2002;47(s1b): 682–685.
Nicolela, Marcelo T and Hnik, Peter and Drance, Stephen M. Scanning laser Doppler flowmeter study of retinal and optic disk blood flow in glaucomatous patients. American journal of ophthalmology, 1996;122(6): 775–783.
Wang, Yimin and Lu, Ake and Gil-Flamer, John and Tan, Ou and Izatt, Joseph A and Huang, David. Measurement of total blood flow in the normal human retina using Doppler Fourier-domain optical coherence tomography. British Journal of Ophthalmology, 2009;93(5): 634–637.
Izhaky, D., Nelson, D. A., Burgansky-Eliash, Z., and Grinvald, A. Functional imaging using the retinal function imager: direct imaging of blood velocity, achieving fluorescein angiography-like images without any contrast agent, qualitative oximetry, and functional metabolic signals. Japanese journal of ophthalmology, 2009;53(4): 345–351.
Caprioli, Joseph and Coleman, Anne L and others. Blood pressure, perfusion pressure, and glaucoma. American journal of ophthalmology, 2010;145(5): 704–712.
Weinreb, Robert N and Harris, Alon. Ocular blood flow in glaucoma. 2009,6.
Al-Diri, Bashir and Hunter, Andrew and Steel, David. An active contour model for segmenting and
measuring retinal vessels. IEEE Transactions on Medical imaging, 2009;28(9): 1488–1497.
Calivá, Francesco and Aletti, Matteo and Al-Diri, Bashir and Hunter, Andrew. A new tool to connect blood vessels in fundus retinal images. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015; 4343–4346.
Wong, T. Y., Klein, R., Klein, B. E., Meuer, S. M., and Hubbard, L. D. Retinal vessel diameters and their associations with age and blood pressure. Investigative ophthalmology and visual science, 2003;44(11):
Pries,ARand Neuhaus,Dand Gaehtgens, P. Blood viscosity in tube flow: dependence on diameter and hematocrit. American Journal of Physiology-Heart and Circulatory Physiology, 1992;263(6): H1770–H1778.
Pries, AR and Ley, K and Claassen, M and Gaehtgens, P. Red cell distribution at microvascular bifurcations. Microvascular research, 1989;38(1): 81–101.
Aletti,Matteo and Gerbeau, Jean-Frédéric and Lombardi, Damiano. Modeling autoregulation in three dimensional simulations of retinal hemodynamics. Journal for Modeling in Ophthalmology, 2015;1.
Causin, P., Guidoboni, G., Malgaroli, F., Sacco, R., and Harris, A. Blood flow mechanics and oxygen transport and delivery in the retinal microcirculation: multiscale mathematical modeling and numerical simulation. Biomechanics and modeling in mechanobiology, 2016;15(3): 525–542.
Fuchsjäger-Mayrl, Gabriele and Polak, Kaija and Luksch, Alexandra and Polska, Elzbieta and Dorner, Guido T and Rainer, Georg and Eichler, Hans-Georg and Schmetterer, Leopold. Retinal blood flow and systemic blood pressure in healthy young subjects. Graefe’s archive for clinical and experimental ophthalmology, 2001;239(9): 673–677.
Pemp, Berthold and Cherecheanu, Alina-Popa and Garhofer, Gerhard and Schmetterer, Leopold. Calculation
of central retinal artery diameters from non-invasive ocular haemodynamic measurements in type 1 diabetes patients. Acta ophthalmologica, 2015;91(5): e348–e352.
Kaiser, Hedwig J and Schoetzau, Andreas and Flammer, Josef. Blood flow velocity in the extraocular vessels in chronic smokers. British journal of ophthalmology, 1997;81(2): 133–135.
Murray, Cecil D. The physiological principle of minimum work I. The vascular system and the cost of blood volume. Proceedings of the National Academy of Sciences, 1926;12(3): 207–214.
Bates, Douglas. Computational methods for mixed models. LME4: Mixed-Eects Modeling with R, 2014; 99–118.
Akaike, Hirotogu. Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike. Springer, 1998; 199–213.
Satterthwaite, Franklin E. An approximate distribution of estimates of variance components. Biometrics bulletin, 1946;2(6): 110–114.
Tukey, John W. Comparing individual means in the analysis of variance. Biometrics, 1949; 99–114.
Bursell, Sven-Erik and Clermont, Allen C and Kinsley, Brendan T and Simonson, Donald C and Aiello, Lloyd M and Wolpert, Howard A. Retinal blood flow changes in patients with insulin-dependent diabetes mellitus and no diabetic retinopathy. Investigative ophthalmology & visual science, 1996;37(5): 886–897.
Grunwald, Juan E and DuPont, Joan and Riva, Charles E. Retinal haemodynamics in patients with early diabetes mellitus. British journal of ophthalmology, 1996;80(4): 327–331.
Feke, GT and Buzney, Sheldon M and Ogasawara, Hironobu and Fujio, Naoki and Goger, Douglas G and Spack, Norman P and Gabbay, Kenneth H. Retinal circulatory abnormalities in type 1 diabetes. Investigative ophthalmology & visual science, 1994;35(7): 2968–2975.