Abstract
Hypertension-related diseases have widespread effects on the systemic microvasculature, with particularly significant impacts on the retinal vascular system. As a non-invasive window to observe vascular abnormalities, fundus imaging plays an important role in the diagnosis and prediction of hypertension-related conditions. In recent years, deep learning (DL) has rapidly advanced in the field of color fundus photography (CFP) analysis, demonstrating strong potential in vessel segmentation, artery/vein classification, lesion detection, and systemic disease prediction. This review systematically summarizes recent progress in DL-based fundus analysis for hypertension-related diseases, focusing on hypertensive retinopathy analysis, automated diagnosis of ocular conditions, and cardiovascular risk prediction. Studies have shown that DL can accurately extract retinal vascular structures and pathological features, offering reliable support for early screening and risk stratification of hypertension-related diseases. Nonetheless, current models still face challenges in generalizability, robustness to low-quality images, and clinical interpretability. Future research should emphasize multimodal data integration, lightweight model design, and clinical validation to promote the real-world application of these technologies in the management of hypertension-related diseases.