Retinal imaging for cerebrovascular risk stratification: evidence, limitations, and practical thresholds for the ophthalmologist.

backgroundThe retina shares developmental origin, microvascular anatomy, and barrier physiology with the brain, making non-invasive retinal imaging a candidate window onto cerebrovascular burden. Despite a growing body of association data, translational claims in this field have frequently outpaced the evidence, and clinically actionable guidance for the practising ophthalmologist remains sparse. This review asks a deliberately narrow question: which retinal imaging domains provide evidence mature enough to inform clinical decisions about cerebrovascular risk, and which remain investigational?

methodsNarrative review of the published literature on five retinal imaging domains: (1) classical fundus signs and quantitative vessel-calibre analysis; (2) retinal vessel geometry including fractal dimension and tortuosity; (3) optical coherence tomography (OCT) structural analysis; (4) optical coherence tomography angiography (OCTA); and (5) artificial intelligence (AI) and oculomics. Evidence was appraised with emphasis on study design, population size, confounding control, external validity, and translational distance from clinical practice. The literature search was updated to March 2026.

resultsClassical retinal microvascular abnormalities and quantitative vessel-calibre metrics carry the strongest and most reproducible evidence base, particularly for cumulative vascular burden, lacunar stroke phenotypes, and cerebral small-vessel disease (cSVD). Retinal vessel fractal dimension adds incremental signal for the cSVD phenotype, with recent Mendelian randomisation data suggesting causal rather than purely associative relationships between retinal vascular morphology and stroke. OCT structural analysis shows consistent associations with ischaemic stroke and cSVD-related neurodegeneration, but the absence of validated clinical thresholds limits immediate applicability. OCTA deepens neurovascular phenotyping but is constrained by device heterogeneity, segmentation variability, and ocular confounding. AI-based oculomics, including foundation-model approaches, demonstrates credible discrimination for silent brain infarction and stroke-risk enrichment, though no randomised controlled trial has yet demonstrated that retinal AI improves cerebrovascular outcomes, and current translational limits remain substantial.

conclusionsRetinal imaging is best positioned as a neurovascular phenotyping tool rather than a stand-alone stroke prediction instrument. The ophthalmologist's highest-value contribution is recognising when embolic, asymmetric, clustered, or disproportionate retinal findings should lower the threshold for broader vascular assessment or neurological referral. Integration with Doppler ultrasound vascular assessment - including ophthalmic artery flow evaluation, carotid haemodynamics, and orbital haemodynamic asymmetry - substantially extends the clinical reach of retinal phenotyping and is highlighted as a mechanistically complementary dimension of extracranial cerebrovascular evaluation. This review provides a practical, pattern-based framework applicable in routine ophthalmic care.