Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning
Author:
Corresponding Author:

Yong-Jiang Cai. Peking University Shenzhen Hospital, Shenzhen 518036, Guangdong Province, China. caiyj2000@sina.cn; Wen-Qun Xi and Zhe Zhang. Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China. xiwq125@126.com; whypotato@126.com

Fund Project:

Supported by Shenzhen Science and Technology Program (No.JCYJ20220530153604010).

  • Article
  • | |
  • Metrics
  • |
  • Reference [42]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    AIM: To develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes. METHODS: Retinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters. RESULTS: Significant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter (P=0.008), fractal dimension (P=0.000), vein curvature (P=0.003), C-zone artery branching vessel count (P=0.049), C-zone vein branching vessel count (P=0.041), artery branching angle (P=0.005), vein branching angle (P=0.001), artery angle asymmetry degree (P=0.003), vessel length density (P=0.000), and vessel area density (P=0.000), totaling 10 parameters. CONCLUSION: The deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes.

    Reference
    1 ElSayed NA, Aleppo G, Aroda VR, et al. 2. Classification and diagnosis of diabetes: standards of care in diabetes—2023. Diabetes Care 2023;46(Suppl 1):S19-S40.
    2 Echouffo-Tcheugui JB, Perreault L, Ji L, Dagogo-Jack S. Diagnosis and management of prediabetes: a review. JAMA 2023;329(14):1206-1216.
    3 Wang L, Gao P, Zhang M, Huang Z, Zhang D, Deng Q, Li Y, Zhao Z, Qin X, Jin D, Zhou M, Tang X, Hu Y, Wang L. Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013. JAMA 2017;317(24):2515.
    4 Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R, IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843.
    5 Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet 2010;376(9735):124-136.
    6 Gong D, Fang LJ, Cai YX, Chong I, Guo JH, Yan ZC, Shen XL, Yang WH, Wang JT. Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy. Front Endocrinol (Lausanne) 2023;14:1244601.
    7 Auvazian SL, Cano J, Leahy S, Karamian P, Kashani A, Moshfeghi A, Ameri H, Blair NP, Shahidi M. Relating retinal vascular oxygen saturation and microvasculature morphology at progressive stages of diabetic retinopathy. Transl Vis Sci Technol 2021;10(6):4.
    8 Gong D, Chen X, Yang L, Zhang Y, Zhong Q, Liu J, Yan C, Cai Y, Yang W, Wang J. From normal population to prediabetes and diabetes: study of influencing factors and prediction models. Front Endocrinol 2023;14:1225696.
    9 Xu B, Chen J, Zhang S, Shen S, Lan X, Chen Z, Yan Z, Xu B. Association between the severity of diabetic retinopathy and optical coherence tomography angiography metrics. Front Endocrinol 2021;12:777552.
    10 Wang JP, Xu BL, Hua JP, Jiang LG, Wang JT, Yang WH, Tong YH,. Analysis of retinal arteriolar and venular parameters in primary open angle glaucoma. Int J Ophthalmol 2023;16(5):671-679.
    11 Bek T. Regional morphology and pathophysiology of retinal vascular disease. Prog Retin Eye Res 2013;36:247-259.
    12 Sun C, Chen T, Cong J, Wu X, Wang J, Yuan Y. Changes in retinal vascular bifurcation in eyes with myopia. BMC Ophthalmol 2022;22(1):408.
    13 Knudtson M, Lee KE, Hubbard L, Wong T, Klein R, Klein B. Revised formulas for summarizing retinal vessel diameters. Curr Eye Res 2003;27:143-149.
    14 Frost S, Kanagasingam Y, Sohrabi H, Vignarajan J, Bourgeat P, Salvado O, Villemagne V, Rowe CC, MacAulay SL, Szoeke C, Ellis KA, Ames D, Masters CL, Rainey-Smith S, Martins RN, AIBL Research Group. Retinal vascular biomarkers for early detection and monitoring of Alzheimer’s disease. Transl Psychiatry 2013;3(2):e233.
    15 Witt N, Wong TY, Hughes AD, Chaturvedi N, Klein BE, Evans R, McNamara M, Thom SAM, Klein R. Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension 2006;47(5):975-981.
    16 Witt NW, Chapman N, Thom SAM, Stanton AV, Parker KH, Hughes AD. A novel measure to characterise optimality of diameter relationships at retinal vascular bifurcations. Artery Res 2010;4(3):75-80.
    17 You QS, Chan JCH, Ng ALK, Choy BKN, Shih KC, Cheung JJC, Wong JKW, Shum JWH, Ni MY, Lai JSM, Leung GM, Cheung CMG, Wong TY, Wong IYH. Macular vessel density measured with optical coherence tomography angiography and its associations in a large population-based study. Invest Ophthalmol Vis Sci 2019;60(14):4830.
    18 Keel S, Koklanis C, Vukicevic M, Itsiopoulos C, Brazionis L. Diabetes, diabetic retinopathy, and retinal vascular alterations. Asia Pac J Ophthalmol (Phila) 2014;3(3):164-171.
    19 Sasongko MB, Wong TY, Nguyen TT, Cheung CY, Shaw JE, Wang JJ. Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy. Diabetologia 2011;54(9):2409-2416.
    20 Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103(2):167-175.
    21 Li JO, Liu HR, Ting DSJ, Jeon S, Paul Chan RVP, Kim JE, Sim DA, Thomas PBM, Lin HT, Chen YX, Sakomoto T, Loewenstein A, Lam DSC, Pasquale LR, Wong TY, Lam LA, Ting DSW. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res 2021;82:100900.
    22 Yang WH, Shao Y, Xu YW; Expert Workgroup of Guidelines on Clinical Research Evaluation of Artificial Intelligence in Ophthalmology (2023), Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association, Intelligent Medicine Committee of Chinese Medicine Education Association. Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023). Int J Ophthalmol 2023;16(9):1361-1372.
    23 Ji YK, Hua RR, Liu S, Xie CJ, Zhang SC, Yang WH. Intelligent diagnosis of retinal vein occlusion based on color fundus photographs. Int J Ophthalmol 2024;17(1):1-6.
    24 Zunair H, Ben Hamza A. Sharp U-Net: Depthwise convolutional network for biomedical image segmentation. Comput Biol Med 2021;136:104699.
    25 Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-basF. Aggressive posterior retinopathy of prematurity: a pilot study of quantitative analysis of vascular features. Graefes Arch Clin Exp Ophthalmol 2015;253(2): 181-187.
    45 Wang G, Li M, Yun Z, Duan Z, Ma K, Luo Z, Xiao P, Yuan J. A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity. Exp Biol Med (Maywood) 2021;246(20):2222-2229.
    46 Cabrera DeBuc D, Somfai GM, Arthur E, Kostic M, Oropesa S, Mendoza Santiesteban C. Investigating multimodal diagnostic eye biomarkers of cognitive impairment by measuring vascular and neurogenic changes in the retina. Front Physiol 2018;9:1721. B, Rudnicka AR, Owen CG, Barman SA. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 2012;59(9):2538-2548.
    30 Grisan E, Foracchia M, Ruggeri A. A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imag 2008;27(3):310-319.
    31 Cheung CY, Xu DJ, Cheng CY, Sabanayagam C, Tham YC, Yu M, Rim TH, Chai CY, Gopinath B, Mitchell P, Poulton R, Moffitt TE, Caspi A, Yam JC, Tham CC, Jonas JB, Wang YX, Song SJ, Burrell LM, Farouque O, Li LJ, Tan G, Ting DSW, Hsu W, Lee ML, Wong TY. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng 2020;5(6):498-508.
    32 Lei J, Pei C, Wen C, Abdelfattah NS. Repeatability and reproducibility of quantification of superficial Peri-papillary capillaries by four different optical coherence tomography angiography devices. Sci Rep 2018;8:17866.
    33 Guo S, Yin S, Tse G, Li G, Su L, Liu T. Association between caliber of retinal vessels and cardiovascular disease: a systematic review and meta-analysis. Curr Atheroscler Rep 2020;22(4):16.
    34 Zekavat SM, Raghu VK, Trinder M, Ye YX, Koyama S, Honigberg MC, Yu Z, Pampana A, Urbut S, Haidermota S, O’Regan DP, Zhao HY, Ellinor PT, Segrè AV, Elze T, Wiggs JL, Martone J, Adelman RA, Zebardast N, Del Priore L, Wang JC, Natarajan P. Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature. Circulation 2022;145(2):134-150.
    35 Bhuiyan A, Kawasaki R, Lamoureux E, Ramamohanarao K, Wong TY. Retinal artery–vein caliber grading using color fundus imaging. Comput Meth Programs Biomed 2013;111(1):104-114.
    36 Lin F, Zhu PL, Huang F, Li QW, Yuan Y, Gao ZH, Yu P, Lin J, Chen FL. Aortic stiffness is associated with the central retinal arteriolar equivalent and retinal vascular fractal dimension in a population along the southeastern coast of China. Hypertens Res 2015;38(5):342-348.
    37 Sun C, Wang JJ, MacKey DA, Wong TY. Retinal vascular caliber: systemic, environmental, and genetic associations. Surv Ophthalmol 2009;54(1):74-95.
    38 Bhuiyan A, Karmakar C, Kawasaki R, Lamoureux E, Ramamohanarao K, Kanagasingam Y, Wong TY. Retinal artery and venular caliber grading: a semi-automated evaluation tool. Comput Biol Med 2014;44:1-9.
    39 Yuan Y, Ikram MK, Vingerling JR, Jiang S, Lin H, Liu M, Ren L, Gao X. Retinal vascular caliber and metabolic syndrome in a Chinese population. Intern Med J 2012;42(9):1014-1022.
    40 Nadal J, Deverdun J, de Champfleur NM, Carriere I, Creuzot-Garcher C, Delcourt C, Chiquet C, Kawasaki R, Villain M, Ritchie K, Le Bars E, Daien V. Retinal vascular fractal dimension and cerebral blood flow, a pilot study. Acta Ophthalmol 2020;98(1):e63-e71.
    41 Wu L, Gong X, Wang W, Zhang L, Zhou J, Ming X, Yuan M, Huang W, Wang L. Association of retinal fractal dimension and vessel tortuosity with impaired renal function among healthy Chinese adults. Front Med 2022;9:925756.
    42 Sandoval-Garcia E, McLachlan S, Price AH, MacGillivray TJ, Strachan MWJ, Wilson JF, Price JF. Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia 2021;64(10): 2215-2227.
    43 Rim TH, Teo AWJ, Yang HHS, Cheung CY, Wong TY. Retinal vascular signs and cerebrovascular diseases. J Neuro Ophthalmol 2020;40(1):44-59.
    44 Woo R, Paul Chan RV, Vinekar A, Chiang M
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

Yan Jiang, Di Gong, Xiao-Hong Chen,/et al.Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning. Int J Ophthalmol, 2024,17(9):1581-1591

Copy
Share
Article Metrics
  • Abstract:149
  • PDF: 295
  • HTML: 0
  • Cited by: 0
Publication History
  • Received:December 19,2023
  • Revised:May 11,2024
  • Online: August 20,2024