Fhima Jonathan, Van Eijgen Jan, Reiner-Benaim Anat, Beeckmans Lennert, Abramovich Or, Stalmans Ingeborg, Behar Joachim A
Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel.
Department of Applied Mathematics, Technion-IIT, Haifa, Israel.
Ophthalmol Sci. 2025 Mar 28;5(5):100778. doi: 10.1016/j.xops.2025.100778. eCollection 2025 Sep-Oct.
To develop and validate an automated end-to-end methodology for analyzing retinal vasculature in large datasets of digital fundus images (DFIs), aiming to assess the influence of demographic and clinical factors on retinal microvasculature.
This study employs a retrospective cohort design to achieve its objectives.
The research utilized a substantial dataset consisting of 32 768 DFIs obtained from individuals undergoing routine eye examinations. There was no inclusion of a separate control group in this study.
The proposed methodology integrates multiple stages: initial image quality assessment, detection of the optic disc (OD), definition of the region of interest surrounding the OD, automated segmentation of retinal arterioles and venules, and the engineering of digital biomarkers representing vasculature characteristics. To analyze the impact of demographic variables (age, sex) and clinical factors (disc size, primary open-angle glaucoma [POAG]), statistical analyses were performed using linear mixed-effects models.
The primary outcomes measured were changes in the retinal vascular geometry. Special attention was given to evaluating the independent effects of age, sex, disc size, and POAG on the newly engineered microvasculature biomarkers.
The analysis revealed significant independent similarities in the retinal vascular geometry alterations associated with both advanced age and POAG. These findings suggest a potential mechanism of accelerated vascular aging in patients with POAG.
This novel methodology allows for the comprehensive and quantitative analysis of retinal vasculature, facilitating the investigation of its correlations with specific diseases. By enabling the reproducible analysis of extensive datasets, this approach provides valuable insights into the state of retinal vascular health and its broader implications for cardiovascular and ocular health. The software developed through this research will be made publicly available upon publication, offering a critical tool for ongoing and future studies in retinal vasculature.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
开发并验证一种自动化的端到端方法，用于分析数字眼底图像（DFI）大型数据集中的视网膜血管系统，旨在评估人口统计学和临床因素对视网膜微血管系统的影响。
本研究采用回顾性队列设计以实现其目标。
该研究使用了一个大量数据集，该数据集由32768张从接受常规眼科检查的个体获得的DFI组成。本研究未纳入单独的对照组。
所提出的方法整合了多个阶段：初始图像质量评估、视盘（OD）检测、OD周围感兴趣区域的定义、视网膜小动脉和小静脉的自动分割，以及代表血管特征的数字生物标志物的构建。为了分析人口统计学变量（年龄、性别）和临床因素（视盘大小、原发性开角型青光眼[POAG]）的影响，使用线性混合效应模型进行了统计分析。
测量的主要结局是视网膜血管几何形状的变化。特别关注评估年龄、性别、视盘大小和POAG对新构建的微血管系统生物标志物的独立影响。
分析显示，与高龄和POAG相关的视网膜血管几何形状改变存在显著的独立相似性。这些发现提示了POAG患者血管加速老化的潜在机制。
这种新颖的方法允许对视网膜血管系统进行全面和定量分析，有助于研究其与特定疾病的相关性。通过对大量数据集进行可重复分析，该方法为视网膜血管健康状况及其对心血管和眼部健康的更广泛影响提供了有价值的见解。通过本研究开发的软件将在发表后公开提供，为视网膜血管系统的当前和未来研究提供关键工具。
在本文末尾的脚注和披露中可能会发现专有或商业披露。
