Research progress on choroidal layer changes in eyes with retinal vein occlusion_Chinese Journal of Ocular Fundus Diseases

Authors：

Liu Chenxing ^1,2 , Chen Ziyang ³ , Ye Zhaoda ² , Chen Sheng ² ,  Hu Yanhong ²

1. Graduate School, Fujian University of Traditional Chinese Medicine, Fuzhou 350003, China;
2. Department of Ophthalmology, The Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou 350003, China;
3. Chinese Medicine Clinnic, Fujian Academy of Chinese Medical Sciences, Fuzhou 350003, China;

Corresponding author：

Hu Yanhong, Email: 12212022@163.com

Keywords：

Retinal vein occlusion; choroidal thickness; Choroidal blood flow; Optical coherence tomography; Review

DOI：

10.3760/cma.j.cn511434-20241008-00370

Video：

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Retinal vein occlusion (RVO) is a serious retinal vascular disease, often accompanied by systemic cardiovascular and cerebrovascular diseases, with ocular changes such as macular oedema, retinal ischemia, and even neovascularisation formation, which, as a common slow disease of the fundus, seriously affects people's vision and quality of life. With the development of optical coherence tomography, the research on the role of the choroid in RVO's onset and progression has gradually attracted people's attention. Currently, observational metrics for changes in the choroidal layer of the RVO eyes have expanded from two-dimensional thickness to three-dimensional parameters such as volume, blood flow density, and blood flow velocity. Some studies have also incorporated artificial intelligence algorithm technologies. In the future, there remains a need to further enrich data on the choroidal layer of the fundus of the RVO eyes to enhance comprehensive understanding of RVO, providing new insights for its clinical prevention and treatment.

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2. 中华医学会眼科学分会眼底病学组, 中国医师协会眼科医师分会眼底病学组. 我国主要眼底病慢病管理专家共识—基于改良版德尔菲法制订[J]. 中华眼底病杂志, 2024, 40(4): 253-263. DOI: 10.3760/cma.j.cn511434-20240220-00076.Fundus Fundus Disease Group of Ophthalmological Society of Chinese Medical Association, Fundus DiseaseGroup of Ophthalmologist Branch of Chinese Medical Doctor Association. National consensus on the management of major chronic fundus diseases in China: a modified Delphiapproach[J]. Chin J Ocul Fundus Dis, 2024, 40(4): 253-263. DOI: 10.3760/cma.j.cn511434-20240220-00076.
3. Fragiotta S, Scuderi L, Iodice CM, et al. Choroidal vasculature changes in age-related macular degeneration: from a molecular to a clinical perspective[J/OL]. Int J Mol Sci, 2022, 23(19): 12010[2022-10-09]. https://pubmed.ncbi.nlm.nih.gov/36233311/. DOI: 10.3390/ijms231912010.
4. 黄厚斌. 视网膜脉络膜的血流调控特点[J]. 眼科, 2024, 33(1): 1-8. DOI: 10.13281/j.cnki.issn.1004-4469.2024.01.001.Huang HB. Circulation control characteristics of retinal and choroid[J]. Ophthalmol CHN, 2024, 33(1): 1-8. DOI: 10.13281/j.cnki.issn.1004-4469.2024.01.001.
5. Brinks J, van Dijk EHC, Klaassen I, et al. Exploring the choroidal vascular labyrinth and its molecular and structural roles in health and disease[J/OL]. Prog Retin Eye Res, 2022, 87: 100994[2021-07-17]. https://pubmed.ncbi.nlm.nih.gov/34280556/. DOI: 10.1016/j.preteyeres.2021.100994.
6. Zheng G, Li J, Zhou Y, et al. Outer retina and choroid as potential imaging markers for evaluation of neural impairment in early type 2 diabetic patients[J/OL]. Eur J Ophthalmol, 2024, 11: 11206721241258637(20124-06-11)[2024-10-08]. https://pubmed.ncbi.nlm.nih.gov/38862403/. DOI: 10.1177/11206721241258637. [published online ahead of print].
7. Kwon DH, Kim YC, Kang KT. Clinical significance of choroidal thickness in eyes with ocular ischemic syndrome[J]. Korean J Ophthalmol, 2022, 36(1): 66-73. DOI: 10.3341/kjo.2021.0155.
8. Liu X, Bi L, Xu Y, et al. Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images[J]. Biomed Opt Express, 2019, 10(4): 1601-1612. DOI: 10.1364/BOE.10.001601.
9. Akatsuka M, Sugiyama E. Precision at the bedside: practical efficacy of clockwise catheter torque for accurate tip positioning of peripherally inserted central catheters[J/OL]. Cureus, 2023, 15(12): e50766[2023-12-19]. https://pubmed.ncbi.nlm.nih.gov/38239529/. DOI: 10.7759/cureus.50766.
10. Li D, Wang Z, Chen W, et al. Quantitative analysis of perfusion characteristics using contrast-enhanced ultrasound in patients with choroidal metastasis[J]. Ophthalmic Res, 2022, 65(1): 86-93. DOI: 10.1159/000510777.
11. Dhirachaikulpanich D, Li X, Porter LF, et al. Integrated microarray and RNAseq transcriptomic analysis of retinal pigment epithelium/choroid in age-related macular degeneration[J/OL]. Front Cell Dev Biol, 2020, 8: 808[2020-08-21]. https://pubmed.ncbi.nlm.nih.gov/32984320/. DOI: 10.3389/fcell.2020.00808.
12. Parobková V, Kompaníková P, Lázňovský J, et al. ChOP-CT: quantitative morphometrical analysis of the Hindbrain choroid plexus by X-ray micro-computed tomography[J]. Fluids Barriers CNS, 2024, 21(1): 9. DOI: 10.1186/s12987-023-00502-8.
13. Almalki YE, Ali MU, Kallu KD, et al. Isolated convolutional-neural-network-based deep-feature extraction for brain tumor classification using shallow classifier[J/OL]. Diagnostics (Basel), 2022, 12(8): 1793[2022-07-24]. https://pubmed.ncbi.nlm.nih.gov/35892504/. DOI: 10.3390/diagnostics12081793.
14. Aziz AZB, Adams J, Elhabian S. Progressive deepSSM: training methodology for image-to-shape deep models[J]. Shape Med Imaging, 2023, 14350: 157-172. DOI: 10.1007/978-3-031-46914-5_13.
15. Guo J, Cao W, Nie B, et al. Unsupervised learning composite network to reduce training cost of deep learning model for colorectal cancer diagnosis[J]. IEEE J Transl Eng Health Med, 2022, 11: 54-59. DOI: 10.1109/JTEHM.2022.3224021.
16. Zhang J, Liu Q, Han X. Dynamic sub-route-based self-adaptive beam search Q-learning algorithm for traveling salesman problem[J/OL]. PLoS One, 2023, 18(3): e0283207[2023-04-21]. https://pubmed.ncbi.nlm.nih.gov/36943840/. DOI: 10.1371/journal.pone.0283207.
17. Wu S, Wang N, Ao W, et al. Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer[J]. Abdom Radiol, 2024, 49(9): 3003-3014. DOI: 10.1007/s00261-024-04232-9.
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