关注视网膜血管成像分析，提升对神经系统疾病的全面认识

doi:10.3969/j.issn.1673-5765.2024.11.001

摘要/Abstract

摘要： 视网膜血管与脑血管在胚胎学起源、解剖学特征和生理学特性等方面均具有相似性和共同性。近年来，视网膜血管成像与分析技术快速发展，通过应用眼底照相、光学相干断层扫描血管成像及多模视网膜血管成像技术，结合后处理分析和（或）人工智能，为神经科学的深入研究提供了新的途径和手段。视网膜微血管可作为多种神经系统疾病分层、评估和治疗监测的窗口，补充现有神经影像技术在疾病诊疗中的不足。关注视网膜血管成像分析的进展及临床应用策略，有助于提升对神经系统疾病的全面认识和辅助临床精准决策。

关键词: 视网膜血管; 神经系统疾病; 成像; 分析

Abstract: The retinal and cerebral vessels share similarities and commonalities in embryological origin, anatomical characteristics, and physiological properties. In recent years, retinal angiography and analysis technologies have rapidly developed. By applying fundus photography, optical coherence tomography angiography, and multimodal retinal angiography technologies, combined with post-processing analysis and/or artificial intelligence, new pathways and means have been provided for in-depth research in neuroscience. Retinal microvessels can serve as a window for stratification, evaluation, and therapeutic monitoring of various neurological diseases, supplementing the shortcomings of existing neuroimaging techniques in disease diagnosis and treatment. Focusing on the advances in retinal angiography analysis and clinical application strategies can enhance a comprehensive understanding of neurological diseases and assist in making precise clinical decisions.

Key words: Retinal vessel; Neurological disease; Imaging; Analysis

中图分类号:

吴松笛. 关注视网膜血管成像分析，提升对神经系统疾病的全面认识[J]. 中国卒中杂志, 2024, 19(11): 1239-1245.

WU Songdi. Focusing on Retinal Angiography and Analysis to Enhance the Comprehensive Understanding of Neurological Diseases[J]. Chinese Journal of Stroke, 2024, 19(11): 1239-1245.

参考文献

[1] LONDON A，BENHAR I，SCHWARTZ M. The retina as a window to the brain-from eye research to CNS disorders[J]. Nat Rev Neurol，2013，9（1）：44-53.
[2] KASHANI A H，ASANAD S，CHAN J W，et al. Past，present and future role of retinal imaging in neurodegenerative disease[J/OL]. Prog Retin Eye Res，2021，83：100938[2024-10-10]. https://doi.org/10.1016/j.preteyeres.2020.100938.
[3] HANSSEN H，STREESE L，VILSER W. Retinal vessel diameters and function in cardiovascular risk and disease[J/OL]. Prog Retin Eye Res，2022，91：101095[2024-10-10]. https://doi.org/10.1016/j.preteyeres.2022.101095.
[4] REPO O，JUONALA M，NIINIKOSKI H，et al. Randomized 20-year infancy-onset dietary intervention，life-long cardiovascular risk factors and retinal microvasculature[J]. Eur Heart J，2024，45（33）：3072-3085.
[5] WU J H，LIU T Y A. Application of deep learning to retinal-image-based oculomics for evaluation of systemic health：a review[J/OL]. J Clin Med，2022，12（1）：152[2024-10-10]. https://doi.org/10.3390/jcm12010152.
[6] CHEUNG C Y，IKRAM M K，CHEN C，et al. Imaging retina to study dementia and stroke[J/OL]. Prog Retin Eye Res，2017，57：89-107[2024-10-10]. https://doi.org/10.1016/j.preteyeres.2017.01.001.
[7] LEE J Y，MARTIN-BASTIDA A，MURUETA-GOYENA A，et al. Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease[J]. Nat Rev Neurol，2022，18（4）：203-220.
[8] 孙功鹏，王晓玲，徐立璋，等. 应用人工智能识别超广角眼底照相多病种的初步研究[J]. 中华眼底病杂志，2022，38（2）：132-138.
SU G P，WANG X L，XU L Z，et al. Preliminary study on the application of artificial intelligence to identify multiple diseases in ultra-widefield fundus images[J]. Chin J Ocul Fundus Dis，2022，38（2）：132-138.
[9] 魏文斌，邵蕾. 充分发挥激光眼底照相的临床应用价值，不断提升眼底病诊断水平[J]. 中华眼底病杂志，2020，36（5）：333-336.
WEN W B，SHAO L. Give full play to the clinical application value of confocal scanning laser ophthalmoscope imaging to improve the diagnosis of ocular fundus diseases[J]. Chin J Ocul Fundus Dis，2020，36（5）：333-336.
[10] BESENCZI R，TÓTH J，HAJDU A. A review on automatic analysis techniques for color fundus photographs[J/OL]. Comput Struct Biotechnol J，2016，14：371-384[2024-10-10]. https://doi.org/10.1016/j.csbj.2016.10.001.
[11] MOOKIAH M R K，HOGG S，MACGILLIVRAY T J，et al. A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification[J/OL]. Med Image Anal，2021，68：101905[2024-10-10]. https://doi.org/10.1016/j.media.2020.101905.
[12] KOVÁCS G，FAZEKAS A. A new baseline for retinal vessel segmentation：numerical identification and correction of methodological inconsistencies affecting 100+ papers[J/OL]. Med Image Anal，2022，75：102300[2024-10-10]. https://doi.org/10.1016/j.media.2021.102300.
[13] LIN J，HUANG X R，ZHOU H Y，et al. Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images[J/OL]. Med Image Anal，2023，89：102929[2024-10-10]. https://doi.org/10.1016/j.media.2023.102929.
[14] WU H S，WANG W，ZHONG J F，et al. SCS-Net：a scale and context sensitive network for retinal vessel segmentation[J/OL]. Med Image Anal，2021，70：102025[2024-10-10]. https://doi.org/10.1016/j.media.2021.102025.
[15] SON J，SHIN J Y，KIM H D，et al. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images[J]. Ophthalmology，2020，127（1）：85-94.
[16] CEN L P，JI J，LIN J W，et al. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks[J/OL]. Nat Commun，2021，12（1）：4828[2024-10-10]. https://doi.org/10.1038/s41467-021-25138-w.
[17] ZEKAVAT S M，RAGHU V K，TRINDER M，et al. Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature[J]. Circulation，2022，145（2）：134-150.
[18] RUDNICKA A R，WELIKALA R，BARMAN S，et al. Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality，myocardial infarction and stroke[J]. Br J Ophthalmol，2022，106（12）：1722-1729.
[19] GIRACH Z，SARIAN A，MALDONADO-GARCÍA C，et al. Retinal imaging for the assessment of stroke risk：a systematic review[J]. J Neurol，2024，271（5）：2285-2297.
[20] ONG S S，PEAVEY J J，HIATT K D，et al. Association of fractal dimension and other retinal vascular network parameters with cognitive performance and neuroimaging biomarkers：the multi-ethnic study of atherosclerosis（MESA）[J]. Alzheimers Dement，2024，20（2）：941-953.
[21] FUJIMOTO J，SWANSON E. The development，commercialization，and impact of optical coherence tomography[J/OL]. Invest Ophthalmol Vis Sci，2016，57（9）：OCT1-OCT13[2024-10-10]. https://doi.org/10.1167/iovs.16-19963.
[22] 陆方，佘凯芩，梁莉聪. 充分发挥光相干断层扫描及其血管成像的临床应用价值，不断提升神经眼科疾病的诊治水平[J]. 中华眼底病杂志，2021，37（3）：169-172.
LU F，SHE K Q，LIANG L C. Applying optical coherence tomography and optical coherence tomography angiography to improve the diagnosis and treatment of neuro-ophthalmic diseases[J]. Chin J Ocul Fundus Dis，2021，37（3）：169-172.
[23] AUGUSTIN A J，ATORF J. The value of optical coherence tomography angiography（OCT-A）in neurological diseases[J/OL]. Diagnostics（Basel），2022，12（2）：468[2024-10-10]. https://doi.org/10.3390/diagnostics12020468.
[24] CHUA J，TAN B Y，WONG D，et al. Optical coherence tomography angiography of the retina and choroid in systemic diseases[J/OL]. Prog Retin Eye Res，2024，103：101292[2024-10-10]. https://doi.org/10.1016/j.preteyeres.2024.101292.
[25] HORMEL T T，JIA Y. OCT angiography and its retinal biomarkers [Invited][J]. Biomed Opt Express，2023，14（9）：4542-4566.
[26] HORMEL T T，HWANG T S，BAILEY S T，et al. Artificial intelligence in OCT angiography[J/OL]. Prog Retin Eye Res，2021，85：100965[2024-10-10]. https://doi.org/10.1016/j.preteyeres.2021.100965.
[27] WANG J，HORMEL T T，GAO L Q，et al. Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning[J]. Biomed Opt Express，2020，11（2）：927-944.
[28] ZANG P X，GAO L Q，HORMEL T T，et al. DcardNet：diabetic retinopathy classification at multiple levels based on structural and angiographic optical coherence tomography[J]. IEEE Trans Biomed Eng，2021，68（6）：1859-1870.
[29] HOU X Y，JIE C H，LIU Z Q，et al. Changes in the retina and choroid in patients with internal carotid artery stenosis：a systematic review and meta-analysis[J/OL]. Front Neurosci，2024，18：1368957[2024-10-10].
https://doi.org/10.3389/fnins.2024.1368957.
[30] XIONG Z W，KWAPONG W R，LIU S Y，et al. Association of retinal biomarkers with the subtypes of ischemic stroke and an automated classification model[J/OL]. Invest Ophthalmol Vis Sci，2024，65（8）：50[2024-10-10]. https://doi.org/10.1167/iovs.65.8.50.
[31] KWAPONG W R，TANG F，LIU P，et al. Choriocapillaris reduction accurately discriminates against early-onset Alzheimer’s disease[J]. Alzheimers Dement，2024，20（6）：4185-4198.
[32] VISITSATTAPONGSE S，MANEERAT A，TRINAVARAT A，et al. Feature-based classification of mild cognitive impairment and Alzheimer’s disease based on optical coherence tomographic angiographic image[J/OL]. Sensors（Basel），2024，24（16）：5192[2024-10-10]. https://doi.org/10.3390/s24165192.
[33] LU H Z，KASHANI A H，ARFANAKIS K，et al. MarkVCID cerebral small vessel consortium：Ⅱ. Neuroimaging protocols[J]. Alzheimers Dement，2021，17（4）：716-725.
[34] LI M C，HUANG K，XU Q Z，et al. OCTA-500：a retinal dataset for optical coherence tomography angiography study[J/OL]. Med Image Anal，2024，93：103092[2024-10-10]. https://doi.org/10.1016/j.media.2024.103092.
[35] KENNEY R，LIU M，HASANAJ L，et al. Normative data and conversion equation for spectral-domain optical coherence tomography in an international healthy control cohort[J]. J Neuroophthalmol，2022，42（4）：442-453.
[36] GHENCIU L A，DIMA M，STOICESCU E R，et al. Retinal imaging-based oculomics：artificial intelligence as a tool in the diagnosis of cardiovascular and metabolic diseases[J/OL]. Biomedicines，2024，12（9）：2150[2024-10-10]. https://doi.org/10.3390/biomedicines12092150.