Retina as a window to systemic hypertension: From hypertensive retinopathy to artificial intelligence-driven microvascular biomarkers

Table 1 Landmark epidemiological studies of quantitative retinal vascular parameters and cardiovascular outcomes

Ref.	Cohort	Design and sample	Retinal parameter	Outcome	Principal finding
Sharrett et al[19], 1999	ARIC	Population-based cross-sectional, middle-aged adults	CRAE	Blood pressure	Mean arterial pressure was the dominant systemic determinant of narrower CRAE
Wong et al[54], 2004	ARIC	Prospective cohort	AVR	Incident hypertension	Smaller AVR predicted incident hypertension over 3 years
Ding et al[20], 2014	IPD meta-analysis, 6 cohorts	Individual-participant meta-analysis	CRAE, CRVE	Incident hypertension	Narrower arterioles and wider venules independently predicted incident hypertension
Kawasaki et al[21], 2009	MESA	Multi-ethnic prospective cohort	CRAE	Incident hypertension	OR = 1.34 (95%CI: 1.18-1.52) per 1-SD decrease in CRAE
Smith et al[22], 2004	Blue Mountains Eye Study	Population-based prospective cohort	Arteriolar calibre	5-year incident severe hypertension	Arteriolar narrowing predicted incident severe hypertension
Wong et al[23], 2002	ARIC	Prospective cohort	AVR	Incident coronary heart disease	Association present in women, absent in men
Seidelmann et al[24], 2016	ARIC	Long-term prospective cohort	CRAE, CRVE	Stroke, heart failure, mortality	Vessel calibres predicted long-term ischaemic stroke, heart failure and all-cause mortality
McGeechan et al[25], 2009	IPD meta-analysis	20798 participants, 945 stroke events	CRVE, CRAE	Incident stroke	CRVE: HR = 1.15 (95%CI: 1.05-1.25) per 20 μm; CRAE: HR = 1.00 (95%CI: 0.92-1.08)
McGeechan et al[26], 2009	IPD meta-analysis	Pooled cohorts	CRAE, CRVE	Coronary heart disease	HR = approximately 1.16-1.17 per 20 µm in women; no association in men
Liew et al[29], 2021	-	Population-based	Fractal dimension	Stroke mortality	Stroke mortality 7.7% in lowest vs 1.3% in highest fractal-dimension quartile
Triantafyllou et al[55], 2013	-	Cross-sectional, ABPM sub-phenotypes	AVR	Hypertension phenotype	AVR 0.820 normotension, 0.739 white-coat, 0.736 sustained, 0.716 masked hypertension

ARIC: Atherosclerosis Risk in Communities; MESA: Multi-ethnic study of atherosclerosis; CRAE: Central retinal arteriolar equivalent; CRVE: Central retinal venular equivalent; AVR: Arteriolar-to-venular ratio; IPD: Individual participant data; ABPM: Ambulatory blood pressure monitoring.

Table 2 Comparison of retinal imaging modalities for hypertension-related microvascular assessment

Modality	Principal biomarkers	Resolution/depth	Relative cost	Accessibility	Acquisition burden	Clinical readiness
Non-mydriatic fundus photography	HR grade; CRAE, CRVE, AVR; fractal dimension, tortuosity, branching angle; AI-derived BP and CVD risk	Surface, approximately 10 µm lateral	Low (portable and smartphone systems available)	High; already embedded in national DR screening programmes	Seconds; no dilation; non-specialist operator	Deployable now; the only modality realistically scalable to population screening
Optical coherence tomography	Peripapillary RNFL and macular GC-IPL thickness	Cross-sectional, approximately 5 µm axial	Moderate to high	Widespread in ophthalmology; rare in primary care	1-2 minute; trained operator	Established in ophthalmic practice; not a hypertension screening tool
OCTA	Superficial and deep capillary vessel density; FAZ area; choriocapillaris flow voids	Capillary plexus level, depth-resolved	High	Specialist ophthalmology centres	1-3 minute; motion artefact common; trained operator	Research and specialist use; device-dependent metrics preclude screening
Adaptive optics SLO	Arteriolar wall thickness; wall-to-lumen ratio	Cellular, minute 2 µm	Very high	Few research centres worldwide	Prolonged; expert operator; small field of view	Research tool only

HR: Hypertensive retinopathy; CRAE: Central retinal arteriolar equivalent; CRVE: Central retinal venular equivalent; AVR: Arteriolar-to-venular ratio; BP: Blood pressure; CVD: Cardiovascular disease; DR: Diabetic retinopathy; RNFL: Retinal nerve fibre layer; GC-IPL: Ganglion cell-inner plexiform layer; FAZ: Foveal avascular zone; SLO: Scanning laser ophthalmoscopy; AI: Artificial intelligence; OCTA: Optical coherence tomography angiography.

Table 3 Comparison of key artificial intelligence studies using retinal images in hypertension and cardiovascular risk

Ref.	Dataset (size, provenance)	Task	Architecture	Reported performance	External validation	Potential clinical application
Abbas et al[41], 2021	1400 graded images, private	5-stage HR classification	DenseNet with semantic/instance segmentation	Sensitivity 90.5%, specificity 91.5%, accuracy 92.6%, AUC 0.915	No	HR grading support
Qureshi et al[42], 2022	Private, small	HR detection	Depth-wise separable CNN + SVM	Sensitivity 94%, specificity 96%, accuracy 95%, AUC 0.96	No	HR detection
Bhimavarapu et al[43], 2024	ODIR, INSPIREVR, VICAVR (augmented, 1200 images)	5-class HR severity	Improved CNN + improved SVM	Accuracy up to 98.99%	No	HR severity grading
Suman et al[44], 2025	HRSG, expert-annotated, private	4-class HR severity	ResNet-50 + modified ViT (locality self-attention), decoupled classifier	Accuracy 0.9688, sensitivity 0.9435, specificity 0.9766	No	HR severity grading
Qian et al[8], 2025 (HRDC challenge)	1000 images per task, public benchmark	Hypertension and HR classification	Multiple submitted algorithms	Best Kappa 0.3819 (hypertension), 0.4154 (HR)	Yes (held-out test set)	Benchmarking; reveals reproducibility gap
Poplin et al[45], 2018	284335 patients; independent validation sets	Prediction of systolic BP, sex, MACE	Inception-v3 CNN	SBP MAE 11.23 mmHg; sex AUC 0.97; MACE AUC 0.70 (95%CI: 0.648-0.740) vs SCORE 0.72	Yes	Population risk stratification
Cheung et al[47], 2021 (SIVA-DLS)	> 70000 images, multi-ethnic, multi-country	Automated CRAE/CRVE measurement and CVD risk	Deep-learning vessel measurement system	ICC 0.82-0.95 vs human graders; predicted incident CVD	Yes	Scalable replacement for semi-automated vessel software
Diaz-Pinto et al[48], 2022	United Kingdom Biobank	Myocardial infarction prediction	Multimodal DL (retina + minimal clinical data)	DL-measured arteriolar narrowing: HR 1.64 (95%CI: 1.16-2.32)	Yes	MI risk prediction
Rim et al[49], 2020	Multi-country retinal photographs	Prediction of systemic biomarkers	DL	Multiple biomarkers predicted with variable accuracy	Yes	Opportunistic systemic screening
Rim et al[50], 2021 (RetiCAC)	216152 photographs; Korea, Singapore, United Kingdom	Retinal surrogate for coronary artery calcium	DL	Predicted CVD events; NRI 22%-26% over Pooled Cohort Equation in borderline/intermediate risk	Yes	CVD risk reclassification
Son et al[51], 2020	Korean cohort	High coronary artery calcium score	DL	Discriminated high CAC from fundus images	Partial	CVD risk stratification
Zhang et al[52], 2020	Central China cross-sectional cohort	Hypertension, hyperglycaemia, dyslipidaemia	DL	Moderate discrimination for chronic disease status	No	Community chronic-disease screening
Zhou et al[9], 2023 (RETFound)	1.6 million unlabelled retinal images	Self-supervised foundation model; ocular and systemic tasks	Masked autoencoder (ViT)	Outperformed comparison models for ocular disease and incident systemic disease with fewer labels	Yes	Label-efficient backbone for HR and oculomics tasks

HR: Hypertensive retinopathy; CNN: Convolutional neural network; SVM: Support vector machine; ViT: Vision transformer; AUC: Area under the receiver operating characteristic curve; BP: Blood pressure; SBP: Systolic blood pressure; MAE: Mean absolute error; MACE: Major adverse cardiac events; CVD: Cardiovascular disease; CAC: Coronary artery calcium; ICC: Intraclass correlation coefficient; NRI: Net reclassification improvement; CRAE: Central retinal arteriolar equivalent; CRVE: Central retinal venular equivalent; MI: Myocardial infarction; DL: Deep learning; HRDC: Hypertensive Retinopathy Diagnosis Challenge.