Review and comparison of retinal vessel calibre and geometry software and their application to diabetes, cardiovascular disease, and dementia.
Cardiovascular disease
Dementia
Diabetes
Diabetic retinopathy
Retinal vessel assessment
Software
Journal
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
ISSN: 1435-702X
Titre abrégé: Graefes Arch Clin Exp Ophthalmol
Pays: Germany
ID NLM: 8205248
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
received:
03
10
2022
accepted:
04
02
2023
revised:
06
01
2023
medline:
26
7
2023
pubmed:
22
2
2023
entrez:
21
2
2023
Statut:
ppublish
Résumé
Developments in retinal imaging technologies have enabled the quantitative evaluation of the retinal vasculature. Changes in retinal calibre and/or geometry have been reported in systemic vascular diseases, including diabetes mellitus (DM), cardiovascular disease (CVD), and more recently in neurodegenerative diseases, such as dementia. Several retinal vessel analysis softwares exist, some being disease-specific, others for a broader context. In the research setting, retinal vasculature analysis using semi-automated software has identified associations between retinal vessel calibre and geometry and the presence of or risk of DM and its chronic complications, and of CVD and dementia, including in the general population. In this article, we review and compare the most widely used semi-automated retinal vessel analysis softwares and their associations with ocular imaging findings in common systemic diseases, including DM and its chronic complications, CVD, and dementia. We also provide original data comparing retinal calibre grading in people with Type 1 DM using two softwares, with good concordance.
Identifiants
pubmed: 36801971
doi: 10.1007/s00417-023-06002-7
pii: 10.1007/s00417-023-06002-7
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2117-2133Subventions
Organisme : National Health and Medical Research Council
ID : GNT1079864
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Cheung CY-l, Sabanayagam C, Law AK-p, et al (2017) Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy. Diabetol 60:1770–1781
Arnould L, Binquet C, Guenancia C et al (2018) Association between the retinal vascular network with Singapore" I" Vessel Assessment (SIVA) software, cardiovascular history and risk factors in the elderly: The Montrachet study, population-based study. PLoS ONE 13:e0194694
pubmed: 29614075
pmcid: 5882094
Williams MA, McGowan AJ, Cardwell CR et al (2015) Retinal microvascular network attenuation in Alzheimer’s disease. Alzheimer’s Dement: Diagn, Assess Dis Monit 1:229–235
Wilson CM, Cocker KD, Moseley MJ et al (2008) Computerized analysis of retinal vessel width and tortuosity in premature infants. Invest Ophthalmol Vis Sci 49:3577–3585
pubmed: 18408177
Drobnjak D, Munch IC, Glümer C et al (2017) Relationship between retinal vessel diameters and retinopathy in the Inter99 Eye Study. J Clin Transl Endocrinol 8:22–28
pubmed: 29067255
pmcid: 5651334
Cheung N, Rogers SL, Donaghue KC et al (2008) Retinal arteriolar dilation predicts retinopathy in adolescents with type 1 diabetes. Diabetes Care 31:1842–1846
pubmed: 18523143
pmcid: 2518356
Drobnjak D, Munch IC, Glümer C et al (2016) Retinal vessel diameters and their relationship with cardiovascular risk and all-cause mortality in the Inter99 Eye Study: a 15-year follow-up. J Ophthalmol 2016:1
Kawasaki R, Cheung N, Wang JJ et al (2009) Retinal vessel diameters and risk of hypertension: the Multiethnic Study of Atherosclerosis. J Hypertens 27:2386
pubmed: 19680136
pmcid: 2935621
Parr J, Spears G (1974) General caliber of the retinal arteries expressed as the equivalent width of the central retinal artery. Am J Ophthalmol 77:472–477
pubmed: 4819451
Hubbard LD, Brothers RJ, King WN et al (1999) Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmol 106:2269–2280
Knudtson MD, Lee KE, Hubbard LD et al (2003) Revised formulas for summarizing retinal vessel diameters. Curr Eye Res 27:143–149
pubmed: 14562179
Wilson CM, Wong K, Ng J et al (2012) Digital image analysis in retinopathy of prematurity: a comparison of vessel selection methods. J Am Assoc Pediatr Ophthalmol Strabismus 16:223–228
Kan H, Stevens J, Heiss G et al (2007) Dietary fiber intake and retinal vascular caliber in the Atherosclerosis Risk in Communities Study. Am J Clin Nutr 86:1626–1632
pubmed: 18065579
Ikram MK, de Jong FJ, Vingerling JR et al (2004) Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study. Invest Ophthalmol Vis Sci 45:2129–2134
pubmed: 15223786
Wong TY, Knudtson MD, Klein R et al (2004) Computer-assisted measurement of retinal vessel diameters in the Beaver Dam Eye Study: methodology, correlation between eyes, and effect of refractive errors. Ophthalmol 111:1183–1190
Liew G, Wang JJ, Rochtchina E et al (2014) Complete blood count and retinal vessel calibers. PLoS ONE 9:e102230
pubmed: 25036459
pmcid: 4103855
Cheung CY-l, Zheng Y, Hsu W et al (2011) Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmol 118:812–818
Sasongko MB, Wang JJ, Donaghue KC et al (2010) Alterations in retinal microvascular geometry in young type 1 diabetes. Diabetes Care 33:1331–1336
pubmed: 20299479
pmcid: 2875449
Perez-Rovira A, MacGillivray T, Trucco E et al (2011) VAMPIRE: vessel assessment and measurement platform for images of the REtina. Ann Int Conf IEEE Eng Med Biol Soc IEEE 2011:3391–3394
Jonas JB, Gusek GC, Naumann G (1988) Optic disc, cup and neuroretinal rim size, configuration and correlations in normal eyes. Invest Ophthalmol Vis Sci 29:1151–1158
pubmed: 3417404
Broe R, Rasmussen ML, Frydkjaer-Olsen U et al (2014) Retinal vessel calibers predict long-term microvascular complications in type 1 diabetes: the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987). Diabetes 63:3906–3914
pubmed: 24914239
Quinn N, Jenkins A, Ryan C et al (2021) Imaging the eye and its relevance to diabetes care. J Diabetes Investig 12:897–908. https://doi.org/10.1111/jdi.13462
doi: 10.1111/jdi.13462
pubmed: 33190401
Hao H, Sasongko MB, Wong TY et al (2012) Does retinal vascular geometry vary with cardiac cycle? Invest Ophthalmol Vis Sci 53:5799–5805
pubmed: 22836773
Kumar DK, Aliahmad B, Hao H et al (2013) A method for visualization of fine retinal vascular pulsation using nonmydriatic fundus camera synchronized with electrocardiogram. ISRN Ophthalmol 2013:1
Patton N, Aslam T, MacGillivray T et al (2005) Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat 206:319–348
pubmed: 15817102
pmcid: 1571489
Robertson G, Fleming A, Williams MC et al (2018) Abstract P400: screening for hypertension using retinal vascular calibre in ultra-widefield fundus imaging. Hypertension 72:AP00
Csincsik L, MacGillivray TJ, Flynn E et al (2018) Peripheral retinal imaging biomarkers for Alzheimer’s disease: a pilot study. Ophthalmic Res 59:182–192
pubmed: 29621759
Ting DSW, Cheung CY-L, Lim G et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318:2211–2223
pubmed: 29234807
pmcid: 5820739
Li Z, Keel S, Liu C et al (2018) An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41:2509–2516
pubmed: 30275284
Ting DS, Cheung CY, Nguyen Q et al (2019) Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study. npj Digital Med 2:24
FDA U (2018) FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Silver Spring, DM, Department of Health and Human Services. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-devicedetect-certain-diabetes-related-eye . Accessed 11 Apr 2018
Du X-L, Li W-B, Hu B-J (2018) Application of artificial intelligence in ophthalmology. Int J Ophthalmol 11:1555
pubmed: 30225234
pmcid: 6133903
Saeedi P, Petersohn I, Salpea P et al (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9<sup>th</sup> edition. Diabetes Res Clin Pract 157:107843. https://doi.org/10.1016/j.diabres.2019.107843
doi: 10.1016/j.diabres.2019.107843
pubmed: 31518657
Klein R (1992) Retinopathy in a population-based study. Trans Am Ophthalmol Soc 90:561
pubmed: 1494834
pmcid: 1298449
Yu T, Mitchell P, Berry G et al (1998) Retinopathy in older persons without diabetes and its relationship to hypertension. Arch Ophthalmol 116:83–89
pubmed: 9445212
Benitez-Aguirre P, Craig ME, Sasongko MB et al (2011) Retinal vascular geometry predicts incident retinopathy in young people with type 1 diabetes: a prospective cohort study from adolescence. Diabetes Care 34:1622–1627
pubmed: 21593293
pmcid: 3120178
Yau JWY, Xie J, Lamoureux E et al (2012) Retinal microvascular calibre and risk of incident diabetes: the multi-ethnic study of atherosclerosis. Diabetes Res Clin Pract 95:265–274
pubmed: 22088792
Ding J, Cheung CY, Ikram MK et al (2012) Early retinal arteriolar changes and peripheral neuropathy in diabetes. Diabetes Care 35:1098–1104
pubmed: 22374638
pmcid: 3329839
Benitez-Aguirre PZ, Sasongko MB, Craig ME et al (2012) Retinal vascular geometry predicts incident renal dysfunction in young people with type 1 diabetes. Diabetes Care 35:599–604
pubmed: 22250064
pmcid: 3322713
Wong TY, Islam FA, Klein R et al (2006) Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (MESA). Invest Ophthalmol Vis Sci 47:2341–2350
pubmed: 16723443
Wang L, Wong TY, Sharrett AR et al (2008) Relationship between retinal arteriolar narrowing and myocardial perfusion: multi-ethnic study of atherosclerosis. Hypertension 51:119–126
pubmed: 17998474
Jeganathan VSE, Sabanayagam C, Tai ES et al (2009) Effect of blood pressure on the retinal vasculature in a multi-ethnic Asian population. Hypertens Res 32:975–982
pubmed: 19713968
Cheung CY-l, Tay WT, Ikram MK, et al (2013) Retinal microvascular changes and risk of stroke: the Singapore Malay Eye Study. Stroke 44:2402–2408
Ong Y-T, De Silva DA, Cheung CY et al (2013) Microvascular structure and network in the retina of patients with ischemic stroke. Stroke 44:2121–2127
pubmed: 23715958
von Hanno T, Bertelsen G, Sjølie AK et al (2014) Retinal vascular calibres are significantly associated with cardiovascular risk factors: the Tromsø Eye Study. Acta Ophthalmol 92:40–46
Yip W, Sabanayagam C, Ong PG et al (2016) Joint effect of early microvascular damage in the eye & kidney on risk of cardiovascular events. Sci Rep 6:27442
pubmed: 27273133
pmcid: 4897605
Wang SB, Mitchell P, Liew G et al (2018) A spectrum of retinal vasculature measures and coronary artery disease. Atherosclerosis 268:215–224
pubmed: 29050745
Frost S, Kanagasingam Y, Sohrabi H et al (2013) Retinal vascular biomarkers for early detection and monitoring of Alzheimer’s disease. Transl Psychiatry 3:e233
pubmed: 23443359
pmcid: 3591002
Cheung CY-l, Ong YT, Ikram MK et al (2014) Microvascular network alterations in the retina of patients with Alzheimer’s disease. Alzheimer’s Dement 10:135–142
Ong Y-T, Hilal S, Cheung CY-l et al (2014) Retinal vascular fractals and cognitive impairment. Dement Geriatr Cogn Disord Extra 4:305–313
Cheung CY-l, Ong S, Ikram MK, et al (2014) Retinal vascular fractal dimension is associated with cognitive dysfunction. J Stroke Cerebrovasc Dis 23:43–50
Williams MA, McGowan AJ, Cardwell CR et al (2015) Retinal microvascular network attenuation in Alzheimer’s disease. Alzheimer’s Dement: Diagn, Assess Dis Monit 1:229–235
McGrory S, Ballerini L, Okely JA et al (2019) Retinal microvascular features and cognitive change in the Lothian-Birth Cohort 1936. Alzheimer’s Dement: Diagn, Assess Dis Monit 11:500–509
Cheung CY, Wong WLE, Hilal S et al (2022) Deep-learning retinal vessel calibre measurements and risk of cognitive decline and dementia. Brain Commun 4:fcac212. https://doi.org/10.1093/braincomms/fcac212
doi: 10.1093/braincomms/fcac212
pubmed: 36043139
pmcid: 9416061
Wickremasinghe SS, Rogers SL, Gillies MC et al (2008) Retinal vascular caliber changes after intravitreal triamcinolone treatment for diabetic macular edema. Invest Ophthalmol Vis Sci 49:4707–4711
pubmed: 18599569
Sasongko MB, Wong TY, Donaghue KC et al (2012) Retinal arteriolar tortuosity is associated with retinopathy and early kidney dysfunction in type 1 diabetes. Am J Ophthalmol 153:176-183.e171
pubmed: 21907319
Poon M, Craig ME, Kaur H et al (2013) Vitamin D deficiency is not associated with changes in retinal geometric parameters in young people with type 1 diabetes. J Diabetes Res 2013:1
Chew SK, Taouk Y, Xie J et al (2013) The relationship of retinal vessel caliber with erectile dysfunction in patients with type 2 diabetes. Invest Ophthalmol Vis Sci 54:7234–7239
pubmed: 24114544
Li L-J, Kramer M, Tapp RJ et al (2017) Gestational diabetes mellitus and retinal microvasculature. BMC Ophthalmol 17:4
pubmed: 28100181
pmcid: 5241913
Klein R, Lee KE, Danforth L et al (2018) The relationship of retinal vessel geometric characteristics to the incidence and progression of diabetic retinopathy. Ophthalmol 125:1784–1792
Lim LS, Chee ML, Cheung CY et al (2017) Retinal vessel geometry and the incidence and progression of diabetic retinopathy. Invest Ophthalmol Vis Sci 58:BIO200–BIO205
pubmed: 28750414
Quinn N, Januszewski AS, Brazionis L et al (2022) Fenofibrate, which reduces risk of sight-threatening diabetic retinopathy in type 2 diabetes, is associated with early narrowing of retinal venules: a FIELD trial substudy. Intern Med J 52:676–679. https://doi.org/10.1111/imj.15733
doi: 10.1111/imj.15733
pubmed: 35419960