ArticleTranslational vision science & technology2025
Retinal Microvascular Biomarker Assessment With Automated Algorithm and Semiautomated Software in the Montrachet Dataset.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Derived Retinal Vascular Morphology and Incident Treatment for Sight-Threatening Diabetic Retinopathy.Ophthalmology science · 2026Article
- Predictive Modeling of Coronary Artery Disease Using Color Fundus Photography-Based Features of Retinal Vasculature.Ophthalmology and therapy · 2026Article
- Through the eye to the heart: a scoping review of artificial intelligence in retinal imaging for cardiovascular disease assessment.BMC medical informatics and decision making · 2025Article
- Selecting the Right AI Algorithm for the Job: A Guide for Navigating the AI Jungle in Ophthalmology.Ophthalmology and therapy · 2025Article
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
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Authors and funding
9 authors.
Funding
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Abstract
Purpose: To compare automated and semiautomated methods for the measurement of retinal microvascular biomarkers: the automated retinal vascular morphology (AutoMorph) algorithm and the Singapore "I" Vessel Assessment (SIVA) software. Methods: Analysis of retinal fundus photographs centered on optic discs from the population-based Montrachet Study of adults aged 75 years and older. Comparison and agreement evaluation with intraclass correlation coefficients (ICCs) between SIVA and AutoMorph measures of the central retinal venular and arteriolar equivalent, arteriolar-venular ratio, and fractal dimension. Results: Overall, 1069 fundus photographs were included in this study. The mean age of the patients was 80.04 ± 3.94 years. After the image quality grading process with an optimal threshold, the lowest rejection rate was 51.17% for the AutoMorph analysis (n = 522). The measure of agreement between SIVA and AutoMorph retinal microvascular biomarkers showed a good correlation for vascular complexity (ICC, 0.77-0.47), a poor correlation for vascular calibers (ICC, 0.36-0.23), and no correlation for vascular tortuosity. Significant associations between retinal biomarkers and systemic variables (age, history of stroke, and systolic blood pressure) were consistent between SIVA and AutoMorph. Conclusions: In this dataset, AutoMorph presented a substantial rejection rate. SIVA and AutoMorph provided well-correlated measurements of vascular complexity and caliber with consistent clinical associations. Further comparisons are needed before a transition is made from semiautomated to automated algorithms for the analysis of retinal microvascular biomarkers. Translational Relevance: Open source software needs to be compared with former semiautomated software for retinal microvascular biomarkers assessment before transition in daily clinic and collaborative research.
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