Computer-aided identification of novel ophthalmic artery waveform parameters in healthy subjects and glaucoma patients
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1.
Carichino L, Guidoboni G, Verticchio Vercellin AC, Milano G, Cutolo CA, Tinelli C, De Silvestri A, Lapin S, Gross JC, Siesky BA, Harris A. Computer-aided identification of novel ophthalmic artery waveform parameters in healthy subjects and glaucoma patients. MAIO [Internet]. 2016 Dec. 15 [cited 2024 Oct. 8];1(2):59-6. Available from: https://www.maio-journal.com/index.php/MAIO/article/view/31

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Keywords

color Doppler imaging; glaucoma; image processing; ophthalmic artery; velocity; waveform parameters

Abstract

Purpose: Arterial waveform parameters (WPs) are commonly used to monitor and diagnose systemic diseases. Color Doppler Imaging (CDI) is a consolidated technique to measure blood velocity profile in some of the major ocular vessels. This study proposes a computer-aided manipulation process of ophthalmic artery (OA) CDI images to classify and quantify WPs that might be significant in the assessment of glaucoma.

Methods: Fifty CDI images acquired by four different operators on nine healthy individuals and 38 CDI images of 38 open-angle glaucoma (OAG) patients were considered. An ad-hoc semi-automated image processing code was implemented to detect the digitalized OA velocity waveform and to extract the WPs. Concordance correlation coefficient (CCC), two-sample t-test and Pearson’s correlation coefficient were used to test for similarities, differences and associations among variables.

Results: The OA-CDI images manipulation proposed showed a higher concordance between measured peak systolic velocity (PSV) data and extracted PSV data (0.80≤CCC≤0.98) than on end diastolic velocity (EDV) (0.45≤CCC≤0.63) and resistive index (RI) (0.30≤CCC≤0.58) data. In OAG patients, EDV, RI, subendocardial viability ratio (SEVR), period (T), area ratio (f) and normalized distance between ascending and descending limb (DAD/T) were found statistically correlated to at least one of the following factors: gender, age, ocular medications and year of diagnosis. When compared to healthy individuals, OAG patients OA-CDI profiles showed statistically higher values of f (p < 0.001) and DAD/T (p = 0.002) (p-values corrected by age and gender).

Conclusion: The proposed computer-aided manipulation of OA-CDI images allowed to identify DAD/T as a novel WP that vary significantly among healthy individuals and OAG patients, and among female and male OAG patients. Future studies on longitudinal OAG data are suggested to investigate the potential of DAD/T to predict severity and progression of the disease.

https://doi.org/10.35119/maio.v1i2.31
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References

Costa VP, Harris A, Anderson D, Stodtmeister R, Cremasco F, Kergoat H, et al. Ocular perfusion pressure in glaucoma. Acta ophthalmologica. 2014;92(4):e252-66.

Cherecheanu AP, Garhofer G, Schmidl D, Werkmeister R, Schmetterer L. Ocular perfusion pressure and ocular blood flow in glaucoma. Current opinion in pharmacology. 2013;13(1):36-42.

Yanagi M, Kawasaki R, Wang JJ, Wong TY, Crowston J, Kiuchi Y. Vascular risk factors in glaucoma: a review. Clinical & experimental ophthalmology. 2011;39(3):252-8.

Schmidl D, Garhofer G, Schmetterer L. The complex interaction between ocular perfusion pressure and ocular blood flow - relevance for glaucoma. Experimental eye research. 2011;93(2):141- 55.

Gerber AL, Harris A, Siesky B, Lee E, Schaab TJ, Huck A, et al. Vascular Dysfunction in Diabetes and Glaucoma: A Complex Relationship Reviewed. Journal of glaucoma. 2015;24(6):474-9.

Lee E, Harris A, Siesky B, Schaab T, McIntyre N, Tobe LA, et al. The influence of retinal blood flow on open-angle glaucoma in patients with and without diabetes. European journal of ophthalmology. 2014;24(4):542-9.

Harris A, Chung HS, Ciulla TA, Kagemann L. Progress in measurement of ocular blood flow and relevance to our understanding of glaucoma and age-related macular degeneration. Progress in retinal and eye research. 1999;18(5):669-87.

Pemp B, Schmetterer L. Ocular blood flow in diabetes and age-related macular degeneration. Canadian journal of ophthalmology Journal canadien d'ophtalmologie. 2008;43(3):295-301.

Ciulla TA, Harris A, Martin BJ. Ocular perfusion and age-related macular degeneration. Acta ophthalmologica Scandinavica. 2001;79(2):108-15.

Ehrlich R, Harris A, Kheradiya NS, Winston DM, Ciulla TA, Wirostko B. Age-related macular degeneration and the aging eye. Clinical interventions in aging. 2008;3(3):473-82.

Siesky B, Harris A, Racette L, Abassi R, Chandrasekhar K, Tobe LA, et al. Differences in ocular blood flow in glaucoma between patients of African and European descent. Journal of glaucoma. 2015;24(2):117-21.

Harris A, Jonescu-Cuypers CP, Kagemann L, Ciulla TA, Krieglstein GK. Atlas of Ocular Blood Flow. Vascular Anatomy, Pathophysiology, and Metabolism. Philadelphia, PA: Elsevier; 2010.

Meng N, Liu J, Zhang Y, Ma J, Li H, Qu Y. Color Doppler Imaging Analysis of Retrobulbar Blood Flow Velocities in Diabetic Patients Without or With Retinopathy: A Meta-analysis. Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine. 2014;33(8):1381-9.

Srikanth K, Kumar MA, Selvasundari S, Prakash ML. Colour Doppler Imaging of Ophthalmic Artery and Central Retinal Artery in Glaucoma Patients with and without Diabetes Mellitus. Journal of clinical and diagnostic research : JCDR. 2014;8(4):VC01-VC2.

Suprasanna K, Shetty CM, Charudutt S, Kadavigere R. Doppler evaluation of ocular vessels in patients with primary open angle glaucoma. Journal of clinical ultrasound : JCU. 2014;42(8):486-91.

Abegao Pinto L, Vandewalle E, Willekens K, Marques-Neves C, Stalmans I. Ocular pulse amplitude and Doppler waveform analysis in glaucoma patients. Acta ophthalmologica. 2014;92(4):e280-5.

Jimenez-Aragon F, Garcia-Martin E, Larrosa-Lopez R, Artigas-Martin JM, Seral-Moral P, Pablo LE. Role of color Doppler imaging in early diagnosis and prediction of progression in glaucoma. BioMed research international. 2013;2013:871689.

Querfurth HW, Arms SW, Lichy CM, Irwin WT, Steiner T. Prediction of intracranial pressure from noninvasive transocular venous and arterial hemodynamic measurements: a pilot study. Neurocritical care. 2004;1(2):183-94.

Leoniuk J, Lukasiewicz A, Szorc M, Sackiewicz I, Janica J, Lebkowska U. Doppler ultrasound detection of preclinical changes in foot arteries in early stage of type 2 diabetes. Polish journal of radiology / Polish Medical Society of Radiology. 2014;79:283-9.

Tahmasebpour HR, Buckley AR, Cooperberg PL, Fix CH. Sonographic examination of the carotid arteries. Radiographics : a review publication of the Radiological Society of North America, Inc. 2005;25(6):1561-75.

Correale M, Totaro A, Ieva R, Ferraretti A, Musaico F, Di Biase M. Tissue Doppler imaging in coronary artery diseases and heart failure. Current cardiology reviews. 2012;8(1):43-53.

Kadappu KK, Thomas L. Tissue Doppler imaging in echocardiography: value and limitations. Heart, lung & circulation. 2015;24(3):224-33.

Choi J, Heo R, Hong GR, Chang HJ, Sung JM, Shin SH, et al. Differential effect of 3-dimensional color Doppler echocardiography for the quantification of mitral regurgitation according to the severity and characteristics. Circulation Cardiovascular imaging. 2014;7(3):535-44.

Wunderlich NC, Beigel R, Siegel RJ. Management of mitral stenosis using 2D and 3D echo- Doppler imaging. JACC Cardiovascular imaging. 2013;6(11):1191-205.

He J, Yan G. Research on Ovary Blood Flow Before and After Uterine Artery Embolization with the Application of Color Doppler Blood Imaging. The Journal of reproductive medicine. 2015;60(11- 12):513-20.

Saini AP, Ural S, Pauliks LB. Quantitation of fetal heart function with tissue Doppler velocity imaging-reference values for color tissue Doppler velocities and comparison with pulsed wave tissue Doppler velocities. Artificial organs. 2014;38(1):87-91.

Galassi F, Sodi A, Ucci F, Renieri G, Pieri B, Baccini M. Ocular hemodynamics and glaucoma prognosis: a color Doppler imaging study. Archives of ophthalmology. 2003;121(12):1711-5.

Martinez A, Sanchez M. Predictive value of colour Doppler imaging in a prospective study of visual field progression in primary open-angle glaucoma. Acta ophthalmologica Scandinavica. 2005;83(6):716-22.

Abegao Pinto L, Vandewalle E, De Clerck E, Marques-Neves C, Stalmans I. Ophthalmic artery Doppler waveform changes associated with increased damage in glaucoma patients. Investigative ophthalmology & visual science. 2012;53(4):2448-53.

Parker JR. Algorithms for image processing and computer vision. New York: Wiley Computer Pub.; 1997. xiii, 417 p. p.

Lim JS. Two-dimensional signal and image processing. Englewood Cliffs, N.J.: Prentice Hall; 1990. xvi, 694 p. p.

Savage MT, Ferro CJ, Pinder SJ, Tomson CR. Reproducibility of derived central arterial waveforms in patients with chronic renal failure. Clinical science. 2002;103(1):59-65.

Oliva I, Roztocil K. Toe pulse wave analysis in obliterating atherosclerosis. Angiology. 1983;34(9):610-9.

Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255-68.

Founti P, Harris A, Papadopoulou D, Emmanouilidis P, Siesky B, Kilintzis V, et al. Agreement among three examiners of colour Doppler imaging retrobulbar blood flow velocity measurements. Acta ophthalmologica. 2011;89(8):e631-4.

Harris A, Williamson TH, Martin B, Shoemaker JA, Sergott RC, Spaeth GL, et al. Test/Retest reproducibility of color Doppler imaging assessment of blood flow velocity in orbital vessels. Journal of glaucoma. 1995;4(4):281-6.

Quaranta L, Harris A, Donato F, Cassamali M, Semeraro F, Nascimbeni G, et al. Color Doppler imaging of ophthalmic artery blood flow velocity: a study of repeatability and agreement. Ophthalmology. 1997;104(4):653-8.

Abegao Pinto L, Willekens K, Van Keer K, Shibesh A, Molenberghs G, Vandewalle E, et al. Ocular blood flow in glaucoma - the Leuven Eye Study. Acta ophthalmologica. 2016.

Kapetanakis VV, Chan MP, Foster PJ, Cook DG, Owen CG, Rudnicka AR. Global variations and time trends in the prevalence of primary open angle glaucoma (POAG): a systematic review and meta-analysis. The British journal of ophthalmology. 2016;100(1):86-93.

Vajaranant TS, Nayak S, Wilensky JT, Joslin CE. Gender and glaucoma: what we know and what we need to know. Current opinion in ophthalmology. 2010;21(2):91-9.

Schmidl D, Schmetterer L, Garhofer G, Popa-Cherecheanu A. Gender differences in ocular blood flow. Current eye research. 2015;40(2):201-12.

Akpek EK, Smith RA. Overview of age-related ocular conditions. The American journal of managed care. 2013;19(5 Suppl):S67-75.

Ehrlich R, Kheradiya NS, Winston DM, Moore DB, Wirostko B, Harris A. Age-related ocular vascular changes. Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie. 2009;247(5):583-91.

Wang JC, Bennett M. Aging and atherosclerosis: mechanisms, functional consequences, and potential therapeutics for cellular senescence. Circulation research. 2012;111(2):245-59.

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