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

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.