publication . Other literature type . Article . 2019

Age-related changes of the retinal microvasculature

Nikita V. Orlov; Cristopher Coletta; Freekje van Asten; Yong Qian; Jun Ding; Majd AlGhatrif; Edward Lakatta; Emily Chew; Wai Wong; Anand Swaroop; ...
Open Access
  • Published: 02 May 2019
  • Publisher: Public Library of Science (PLoS)
Abstract
PURPOSE: Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. RESULTS: We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examin...
Subjects
Medical Subject Headings: sense organs
free text keywords: Medicine, R, Science, Q, Research Article, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
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36 references, page 1 of 3

1 Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science. 2016;57(13):5200–6. Epub 2016/10/05. 10.1167/iovs.16-19964 .27701631 [OpenAIRE] [PubMed] [DOI]

2 Niemeijer M, Ginneken Bv, Russel SR, Suttorp-Schulten MSA, Abramoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative Ophthalmology & Visual Science. 2007;48(5):2260–7. Epub 2007/04/27. 10.1167/iovs.06-0996 17460289 [OpenAIRE] [PubMed] [DOI]

3 Cheung CY, Zheng Y, Hsu W, Lee ML, Lau QP, Mitchell P, et al Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology. 2011;118(5):812–8. Epub 2010/12/15. 10.1016/j.ophtha.2010.08.045 .21146228 [OpenAIRE] [PubMed] [DOI]

4 Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR, editors. Automated measurement of retinal vascular tortuosity. Proc AMIA Annu Fall Symposium; 1997. [OpenAIRE]

5 Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR. Measurement and classification of retinal vascular tortuosity. International Journal of Medical Informatics. 1999;53(2–3):239–52. Epub 1999/04/08. .10193892 [PubMed]

6 Sasongko MB, T.Y.W, Donaghue KC, Cheung N, Jenkins AJ, Benitez-Aguirre P, et al Retinal arteriolar tortuosity is associated with retinopathy and early kidney dysfunction in type 1 diabetes. American Journal of Ophthalmology. 2012;153(1):176–83. 10.1016/j.ajo.2011.06.005 WOS:000298312700024. 21907319 [OpenAIRE] [PubMed] [DOI]

7 Owen CG, Rudnicka AR, Nightingale CM, Mullen R, Barman SA, Sattar N, et al Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the Child Heart and Health Study in England (CHASE). Arterioscler Thromb Vasc Biol. 2011;31(8):1933–8. Epub 2011/06/11. 10.1161/ATVBAHA.111.225219 21659645 [OpenAIRE] [PubMed] [DOI]

8 Liew G, Wang JJ, Cheung N, Zhang YP, Hsu W, Lee ML, et al The retinal vasculature as a fractal: methodology, reliability and relationship to blood pressure. Ophthalmology. 2008;115(11):1951–6. 10.1016/j.ophtha.2008.05.029 WOS:000260448900016. 18692247 [OpenAIRE] [PubMed] [DOI]

9 Cheung CY, Ong S, Ikram MK, Ong YT, Chen CP, Venketasubramanian N, et al Retinal vascular fractal dimension is associated with cognitive dysfunction. Journal of Stroke and Cerebrovascular Diseases. 2014;23(1):43–50. 10.1016/j.jstrokecerebrovasdis.2012.09.002 WOS:000329219900015. 23099042 [OpenAIRE] [PubMed] [DOI]

10 Stanton AV, Wasan B, Cerutti A, Ford S, Marsh R, Sever PP, et al Vascular network changes in the retina with age and hypertension. J Hypertens. 1995;13(12 Pt 2):1724–8. Epub 1995/12/01. 8903640 [OpenAIRE] [PubMed]

11 Guo VY, Chan JC, Chung H, Ozaki R, So W, Luk A, et al Retinal Information is Independently Associated with Cardiovascular Disease in Patients with Type 2 diabetes. Sci Rep. 2016;6:19053 Epub 2016/01/13. 10.1038/srep19053 26754623 [OpenAIRE] [PubMed] [DOI]

12 Willia ms MA, McGowan AJ, Cardwell CR, Cheung CY, Craig D, Passmore P, et al Retinal microvascular network attenuation in Alzheimer's disease. Alzheimers Dement (Amst). 2015;1(2):229–35. Epub 2015/12/04. 10.1016/j.dadm.2015.04.001 26634224 [OpenAIRE] [PubMed] [DOI]

13 van Grinsven MJ, Buitendijk GH, Brussee C, van Ginneken B, Hoyng CB, Theelen T, et al Automatic identification of reticular pseudodrusen using multimodal retinal image analysis. Investigative Ophthalmology & Visual Science. 2015;56(1):633–9. Epub 2015/01/13. 10.1167/iovs.14-15019 .25574052 [OpenAIRE] [PubMed] [DOI]

14 Burlina PM, Joshi N, Pekala M, Pacheco K, D., Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmology. 2017;135(11):1170–6. Epub 2017/10/04. 10.1001/jamaophthalmol.2017.3782 28973096 [OpenAIRE] [PubMed] [DOI]

15 Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018 10.1038/s41551-018-0195-0 31015713 [OpenAIRE] [PubMed] [DOI]

36 references, page 1 of 3
Abstract
PURPOSE: Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. RESULTS: We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examin...
Subjects
Medical Subject Headings: sense organs
free text keywords: Medicine, R, Science, Q, Research Article, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
Download fromView all 4 versions
PLoS ONE
Article . 2019
Europe PubMed Central
Other literature type . 2019
PLoS ONE
Article . 2019
Provider: Crossref
PLoS ONE
Article
Provider: UnpayWall
36 references, page 1 of 3

1 Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science. 2016;57(13):5200–6. Epub 2016/10/05. 10.1167/iovs.16-19964 .27701631 [OpenAIRE] [PubMed] [DOI]

2 Niemeijer M, Ginneken Bv, Russel SR, Suttorp-Schulten MSA, Abramoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative Ophthalmology & Visual Science. 2007;48(5):2260–7. Epub 2007/04/27. 10.1167/iovs.06-0996 17460289 [OpenAIRE] [PubMed] [DOI]

3 Cheung CY, Zheng Y, Hsu W, Lee ML, Lau QP, Mitchell P, et al Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology. 2011;118(5):812–8. Epub 2010/12/15. 10.1016/j.ophtha.2010.08.045 .21146228 [OpenAIRE] [PubMed] [DOI]

4 Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR, editors. Automated measurement of retinal vascular tortuosity. Proc AMIA Annu Fall Symposium; 1997. [OpenAIRE]

5 Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR. Measurement and classification of retinal vascular tortuosity. International Journal of Medical Informatics. 1999;53(2–3):239–52. Epub 1999/04/08. .10193892 [PubMed]

6 Sasongko MB, T.Y.W, Donaghue KC, Cheung N, Jenkins AJ, Benitez-Aguirre P, et al Retinal arteriolar tortuosity is associated with retinopathy and early kidney dysfunction in type 1 diabetes. American Journal of Ophthalmology. 2012;153(1):176–83. 10.1016/j.ajo.2011.06.005 WOS:000298312700024. 21907319 [OpenAIRE] [PubMed] [DOI]

7 Owen CG, Rudnicka AR, Nightingale CM, Mullen R, Barman SA, Sattar N, et al Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the Child Heart and Health Study in England (CHASE). Arterioscler Thromb Vasc Biol. 2011;31(8):1933–8. Epub 2011/06/11. 10.1161/ATVBAHA.111.225219 21659645 [OpenAIRE] [PubMed] [DOI]

8 Liew G, Wang JJ, Cheung N, Zhang YP, Hsu W, Lee ML, et al The retinal vasculature as a fractal: methodology, reliability and relationship to blood pressure. Ophthalmology. 2008;115(11):1951–6. 10.1016/j.ophtha.2008.05.029 WOS:000260448900016. 18692247 [OpenAIRE] [PubMed] [DOI]

9 Cheung CY, Ong S, Ikram MK, Ong YT, Chen CP, Venketasubramanian N, et al Retinal vascular fractal dimension is associated with cognitive dysfunction. Journal of Stroke and Cerebrovascular Diseases. 2014;23(1):43–50. 10.1016/j.jstrokecerebrovasdis.2012.09.002 WOS:000329219900015. 23099042 [OpenAIRE] [PubMed] [DOI]

10 Stanton AV, Wasan B, Cerutti A, Ford S, Marsh R, Sever PP, et al Vascular network changes in the retina with age and hypertension. J Hypertens. 1995;13(12 Pt 2):1724–8. Epub 1995/12/01. 8903640 [OpenAIRE] [PubMed]

11 Guo VY, Chan JC, Chung H, Ozaki R, So W, Luk A, et al Retinal Information is Independently Associated with Cardiovascular Disease in Patients with Type 2 diabetes. Sci Rep. 2016;6:19053 Epub 2016/01/13. 10.1038/srep19053 26754623 [OpenAIRE] [PubMed] [DOI]

12 Willia ms MA, McGowan AJ, Cardwell CR, Cheung CY, Craig D, Passmore P, et al Retinal microvascular network attenuation in Alzheimer's disease. Alzheimers Dement (Amst). 2015;1(2):229–35. Epub 2015/12/04. 10.1016/j.dadm.2015.04.001 26634224 [OpenAIRE] [PubMed] [DOI]

13 van Grinsven MJ, Buitendijk GH, Brussee C, van Ginneken B, Hoyng CB, Theelen T, et al Automatic identification of reticular pseudodrusen using multimodal retinal image analysis. Investigative Ophthalmology & Visual Science. 2015;56(1):633–9. Epub 2015/01/13. 10.1167/iovs.14-15019 .25574052 [OpenAIRE] [PubMed] [DOI]

14 Burlina PM, Joshi N, Pekala M, Pacheco K, D., Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmology. 2017;135(11):1170–6. Epub 2017/10/04. 10.1001/jamaophthalmol.2017.3782 28973096 [OpenAIRE] [PubMed] [DOI]

15 Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018 10.1038/s41551-018-0195-0 31015713 [OpenAIRE] [PubMed] [DOI]

36 references, page 1 of 3
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