Correlation study of retinal vascular morphological parameters with ischemic stroke_Chinese Journal of Ocular Fundus Diseases

Authors：

Zhao Lu ¹ , Chen Yanan ¹ , Jiang Bin ² , Ling Saiguang ³ ,  Wang Yanling ¹

1. Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China;
2. Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China;
3. EVision technology (Beijing) co. LTD, Beijing 100085, China;

Corresponding author：

Wang Yanling, Email: wangyanling999@vip.sina.com

Keywords：

Ischemic stroke; Retinal vessel caliber; Fractal dimension; Vascular density

DOI：

10.3760/cma.j.cn511434-20220919-00502

Video：

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Objective To observe the morphological characteristics of retinal vessels in patients with ischemic stroke, and to preliminary analyze the correlation between retinal vascular morphological parameters and ischemic stroke. Methods A retrospective study. From May 2015 to May 2017, 73 patients with ischemic stroke (ischemic stroke group) confirmed by examination at the Beijing Friendship Hospital, Capital Medical University were included in this study. In addition, 146 patients were included in the control group. A total of 146 patients with acute stroke who were excluded by head CT and/or magnetic resonance imaging were selected as the control group. Fundus images of patients were collected by nonmydriatic fundus camera. Retinal vascular parameters were measured by artificial intelligence fundus image analysis system, included retinal artery and vein caliber as well as vascular curvature, branching angle, fractal dimension, and density. The morphological characteristics of retinal vessels were compared between the control and ischemic stroke groups, and correlation between the retinal vascular parameters and ischemic stroke was analyzed using binary logistic regression. Results Compared with the control group, the ischemic stroke group had thinner retinal artery caliber, smaller retinal vascular fractal dimension, and lower retinal vascular density; moreover, these differences were statistically significant (t=3.232, 3.502, 3.280; P＜0.05). Vascular fractal dimension [odds ratio (OR)=0.291, 95% confidence interval (CI) 0.160-0.528] and retinal artery caliber (OR=0.924, 95%CI 0.870-0.981) were strongly correlated with ischemic stroke (P＜0.01). Conclusion Compared with the control group, the retinal artery caliber, fractal dimension and retinal vascular density in ischemic stroke group are smaller; decreased retinal artery caliber and fractal dimension are correlated with ischemic stroke.

Citation： Zhao Lu, Chen Yanan, Jiang Bin, Ling Saiguang, Wang Yanling. Correlation study of retinal vascular morphological parameters with ischemic stroke. Chinese Journal of Ocular Fundus Diseases, 2022, 38(12): 1001-1005. doi: 10.3760/cma.j.cn511434-20220919-00502 Copy

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4.	黄茜, 钟跃思, 蔡满航. 肝硬化患者的视网膜血管特征及其可能的诊断价值[J]. 新医学, 2021, 52(4): 255-259. DOI: 10.3969/j.issn.0253-9802.2021.04.006.Huang X, Zhong YS, Cai MH. Retinal vascular features and diagnostic values in patients with liver cirrhosis[J]. New Medicine, 2021, 52(4): 255-259. DOI: 10.3969/j.issn.0253-9802.2021.04.006.
5.	Shao L, Zhang QL, Long TF, et al. Quantitative assessment of fundus tessellated density and associated factors in fundus images using artificial intelligencel[J]. Transl Vis Sci Techno, 2021, 10(9): 23. DOI: 10.1167/tvst.10.9.23.
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7.	徐艺, 凌赛广, 董洲, 等. 一种基于计算机视觉的眼底图像质量评估系统的开发及应用[J]. 中华眼科杂志, 2020, 56(12): 920-927. DOI: 10.3760/cma.j.cn112142-20200409-00257.Xu Y, Ling SG, Dong Z, et al. Development and application of a fundus image quality assessment system based on computer vision technology[J]. Chin J Ophthalmol, 2020, 56(12): 920-927. DOI: 10.3760/cma.j.cn112142-20200409-00257.
8.	Kipli K, Hoque ME, Lim LT, et al. A review on the extraction of quantitative retinal microvascular image feature[J/OL]. Comput Math Methods Med, 2018, 2018: 4019538[2018-07-02]. https://pubmed.ncbi.nlm.nih.gov/30065780/. DOI: 10.1155/2018/4019538.
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11.	Yatsuya H, Folsom AR, Wong TY, et al. Retinal microvascular abnormalities and risk of lacunar stroke: Atherosclerosis Risk in Communities Study[J]. Stroke, 2010, 41(7): 1349-1355. DOI: 10.1161/STROKEAHA.110.580837.
12.	Ikram MK, de Jong FJ, Bos MJ, et al. Retinal vessel caliber and risk of stroke: the Rotterdam Study[J]. Neurology, 2006, 66(9): 1339-1343. DOI: 10.1212/01.wnl.0000210533.24338.ea.
13.	Dumitrascu OM, Demaerschalk BM, Valencia Sanchez C, et al. Retinal microvascular abnormalities as surrogate markers of cerebrovascular ischemic disease: a meta-analysis[J]. J Stroke Cerebrovasc Dis, 2018, 27(7): 1960-1968. DOI: 10.1016/j.jstrokecerebrovasdis.2018.02.041.
14.	Sasongko MB, Wong TY, Nguyen TT, et al. Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy[J]. Diabetologia, 2011, 54(9): 2409-2416. DOI: 10.1007/s00125-011-2200-y.
15.	Shi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease[J]. Stroke Vasc Neurol, 2016, 1(3): 83-92. DOI: 10.1136/svn-2016-000035.
16.	杨振, 段俊国, 蹇文渊, 等. 视网膜血管弯曲度特征的应用研究进展[J]. 中医眼耳鼻喉杂志, 2019, 9(1): 35-39, 43. DOI: 10.3969/j.issn.1674-9006.2019.01.015.Yang Z, Duan JG, Jian WY, et al. Advances in the application of retinal vessel curvature characteristics[J]. J Chin Ophthalmol & Otorhinolaryngol, 2019, 9(1): 35-39, 43. DOI: 10.3969/j.issn.1674-9006.2019.01.015.
17.	裴利, 蹇文渊, 罗圆, 等. 健康人视网膜血管参数变化研究[J]. 眼科新进展, 2019, 39(12): 1129-1132. DOI: 10.13389/j.cnki.rao.2019.0259.Pei L, Jian WY, Luo Y, et al. Retina vessel parameter difference of healthy people[J]. Rec Adv Ophthalmol, 2019, 39(12): 1129-1132. DOI: 10.13389/j.cnki.rao.2019.0259.
18.	Cheung CY, Ong YT, Ikram MK, et al. Microvascular network alterations in the retina of patients with Alzheimer's disease[J]. Alzheimers Dement, 2014, 10(2): 135-142. DOI: 10.1016/j.jalz.2013.06.009.
19.	Kawasaki R, Che Azemin MZ, Kumar DK, et al. Fractal dimension of the retinal vasculature and risk of stroke: a nested case-control study[J]. Neurology, 2011, 76(20): 1766-1767. DOI: 10.1212/WNL.0b013e31821a7d7d.
20.	Lemmens S, Devulder A, Van Keer K, et al. Systematic review on fractal dimension of the retinal vasculature in neurodegeneration and stroke: assessment of a potential biomarker[J]. Front Neurosci, 2020, 14: 16. DOI: 10.3389/fnins.2020.00016.
21.	Ong YT, De Silva DA, Cheung CY, et al. Microvascular structure and network in the retina of patients with ischemic stroke[J]. Stroke, 2013, 44(8): 2121-2127. DOI: 10.1161/STROKEAHA.113.001741.
22.	Wang Y, Wang L, Zhou H, et al. Application research of artificial intelligence screening system for diabetic retinopathy[J/OL]. J Healthc Eng, 2022, 2022: 2185547[2022-01-17]. https://pubmed.ncbi.nlm.nih.gov/35083023/. DOI: 10.1155/2022/2185547.
23.	Li F, Su Y, Lin F, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs[J/OL]. J Clin Invest, 2022, 132(11): e157968[2022-06-01]. https://pubmed.ncbi.nlm.nih.gov/35642636/. DOI: 10.1172/JCI157968.
24.	Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomed Eng, 2018, 2(3): 158-164. DOI: 10.1038/s41551-018-0195-0.
25.	Gupta VB, Chitranshi N, den Haan J, et al. Retinal changes in Alzheimer's disease- integrated prospects of imaging, functional and molecular advances[J/OL]. Prog Retin Eye Res, 2021, 82: 100899[2020-09-02]. https://pubmed.ncbi.nlm.nih.gov/32890742/. DOI: 10.1016/j.preteyeres.2020.100899.
26.	Zhang K, Liu X, Xu J, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images[J]. Nat Biomed Eng, 2021, 5(6): 533-545. DOI: 10.1038/s41551-021-00745-6.
27.	Lau AY, Mok V, Lee J, et al. Retinal image analytics detects white matter hyperintensities in healthy adults[J]. Ann Clin Transl Neurol, 2018, 6(1): 98-105. DOI: 10.1002/acn3.688.

1. Cheung CY, Tay WT, Ikram MK, et al. Retinal microvascular changes and risk of stroke: the Singapore Malay Eye Study[J]. Stroke, 2013, 44(9): 2402-2408. DOI: 10.1161/STROKEAHA.113.001738.
2. 中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组. 中国急性缺血性脑卒中诊治指南2018[J]. 中华神经科杂志, 2018, 51(9): 666-682. DOI: 10.3760/cma.j.issn.1006-7876.2018.09.004.Chinese Society of Neurology, Chinese Stroke Society. Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018[J]. Chin J Neurol, 2018, 51(9): 666-682. DOI: 10.3760/cma.j.issn.1006-7876.2018.09.004.
3. Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment[J]. Stroke, 1993, 24(1): 35-41. DOI: 10.1161/01.str.24.1.35.
4. 黄茜, 钟跃思, 蔡满航. 肝硬化患者的视网膜血管特征及其可能的诊断价值[J]. 新医学, 2021, 52(4): 255-259. DOI: 10.3969/j.issn.0253-9802.2021.04.006.Huang X, Zhong YS, Cai MH. Retinal vascular features and diagnostic values in patients with liver cirrhosis[J]. New Medicine, 2021, 52(4): 255-259. DOI: 10.3969/j.issn.0253-9802.2021.04.006.
5. Shao L, Zhang QL, Long TF, et al. Quantitative assessment of fundus tessellated density and associated factors in fundus images using artificial intelligencel[J]. Transl Vis Sci Techno, 2021, 10(9): 23. DOI: 10.1167/tvst.10.9.23.
6. Xu Y, Wang Y, Liu B, et al. The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients[J]. BMC Ophthalmol, 2019, 19(1): 184. DOI: 10.1186/s12886-019-1196-9.
7. 徐艺, 凌赛广, 董洲, 等. 一种基于计算机视觉的眼底图像质量评估系统的开发及应用[J]. 中华眼科杂志, 2020, 56(12): 920-927. DOI: 10.3760/cma.j.cn112142-20200409-00257.Xu Y, Ling SG, Dong Z, et al. Development and application of a fundus image quality assessment system based on computer vision technology[J]. Chin J Ophthalmol, 2020, 56(12): 920-927. DOI: 10.3760/cma.j.cn112142-20200409-00257.
8. Kipli K, Hoque ME, Lim LT, et al. A review on the extraction of quantitative retinal microvascular image feature[J/OL]. Comput Math Methods Med, 2018, 2018: 4019538[2018-07-02]. https://pubmed.ncbi.nlm.nih.gov/30065780/. DOI: 10.1155/2018/4019538.
9. Wong TY, Klein R, Couper DJ, et al. Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study[J]. Lancet, 2001, 358(9288): 1134-1140. DOI: 10.1016/S0140-6736(01)06253-5.
10. Mitchell P, Wang JJ, Wong TY, et al. Retinal microvascular signs and risk of stroke and stroke mortality[J]. Neurology, 2005, 65(7): 1005-1009. DOI: 10.1212/01.wnl.0000179177.15900.ca.
11. Yatsuya H, Folsom AR, Wong TY, et al. Retinal microvascular abnormalities and risk of lacunar stroke: Atherosclerosis Risk in Communities Study[J]. Stroke, 2010, 41(7): 1349-1355. DOI: 10.1161/STROKEAHA.110.580837.
12. Ikram MK, de Jong FJ, Bos MJ, et al. Retinal vessel caliber and risk of stroke: the Rotterdam Study[J]. Neurology, 2006, 66(9): 1339-1343. DOI: 10.1212/01.wnl.0000210533.24338.ea.
13. Dumitrascu OM, Demaerschalk BM, Valencia Sanchez C, et al. Retinal microvascular abnormalities as surrogate markers of cerebrovascular ischemic disease: a meta-analysis[J]. J Stroke Cerebrovasc Dis, 2018, 27(7): 1960-1968. DOI: 10.1016/j.jstrokecerebrovasdis.2018.02.041.
14. Sasongko MB, Wong TY, Nguyen TT, et al. Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy[J]. Diabetologia, 2011, 54(9): 2409-2416. DOI: 10.1007/s00125-011-2200-y.
15. Shi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease[J]. Stroke Vasc Neurol, 2016, 1(3): 83-92. DOI: 10.1136/svn-2016-000035.
16. 杨振, 段俊国, 蹇文渊, 等. 视网膜血管弯曲度特征的应用研究进展[J]. 中医眼耳鼻喉杂志, 2019, 9(1): 35-39, 43. DOI: 10.3969/j.issn.1674-9006.2019.01.015.Yang Z, Duan JG, Jian WY, et al. Advances in the application of retinal vessel curvature characteristics[J]. J Chin Ophthalmol & Otorhinolaryngol, 2019, 9(1): 35-39, 43. DOI: 10.3969/j.issn.1674-9006.2019.01.015.
17. 裴利, 蹇文渊, 罗圆, 等. 健康人视网膜血管参数变化研究[J]. 眼科新进展, 2019, 39(12): 1129-1132. DOI: 10.13389/j.cnki.rao.2019.0259.Pei L, Jian WY, Luo Y, et al. Retina vessel parameter difference of healthy people[J]. Rec Adv Ophthalmol, 2019, 39(12): 1129-1132. DOI: 10.13389/j.cnki.rao.2019.0259.
18. Cheung CY, Ong YT, Ikram MK, et al. Microvascular network alterations in the retina of patients with Alzheimer's disease[J]. Alzheimers Dement, 2014, 10(2): 135-142. DOI: 10.1016/j.jalz.2013.06.009.
19. Kawasaki R, Che Azemin MZ, Kumar DK, et al. Fractal dimension of the retinal vasculature and risk of stroke: a nested case-control study[J]. Neurology, 2011, 76(20): 1766-1767. DOI: 10.1212/WNL.0b013e31821a7d7d.
20. Lemmens S, Devulder A, Van Keer K, et al. Systematic review on fractal dimension of the retinal vasculature in neurodegeneration and stroke: assessment of a potential biomarker[J]. Front Neurosci, 2020, 14: 16. DOI: 10.3389/fnins.2020.00016.
21. Ong YT, De Silva DA, Cheung CY, et al. Microvascular structure and network in the retina of patients with ischemic stroke[J]. Stroke, 2013, 44(8): 2121-2127. DOI: 10.1161/STROKEAHA.113.001741.
22. Wang Y, Wang L, Zhou H, et al. Application research of artificial intelligence screening system for diabetic retinopathy[J/OL]. J Healthc Eng, 2022, 2022: 2185547[2022-01-17]. https://pubmed.ncbi.nlm.nih.gov/35083023/. DOI: 10.1155/2022/2185547.
23. Li F, Su Y, Lin F, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs[J/OL]. J Clin Invest, 2022, 132(11): e157968[2022-06-01]. https://pubmed.ncbi.nlm.nih.gov/35642636/. DOI: 10.1172/JCI157968.
24. Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomed Eng, 2018, 2(3): 158-164. DOI: 10.1038/s41551-018-0195-0.
25. Gupta VB, Chitranshi N, den Haan J, et al. Retinal changes in Alzheimer's disease- integrated prospects of imaging, functional and molecular advances[J/OL]. Prog Retin Eye Res, 2021, 82: 100899[2020-09-02]. https://pubmed.ncbi.nlm.nih.gov/32890742/. DOI: 10.1016/j.preteyeres.2020.100899.
26. Zhang K, Liu X, Xu J, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images[J]. Nat Biomed Eng, 2021, 5(6): 533-545. DOI: 10.1038/s41551-021-00745-6.
27. Lau AY, Mok V, Lee J, et al. Retinal image analytics detects white matter hyperintensities in healthy adults[J]. Ann Clin Transl Neurol, 2018, 6(1): 98-105. DOI: 10.1002/acn3.688.

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