ReviewVisual computing for industry, biomedicine, and art2025
Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective.
Review in Visual computing for industry, biomedicine, and art, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Initiating Events of Retinopathy in Spontaneously Hypertensive Rats: Microvascular Rarefaction and Functional Deficits.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Adaptive Optics RTX1 Imaging for Early Detection of Retinal Vascular Remodeling in Hypertensive Retinopathy: A Review.Journal of clinical medicine · 2026Review
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- Automated diagnosis of plus form and early stages of ROP using deep learning models.Scientific reports · 2026Article
- Detection of diabetic retinopathy using multicolor image by multimodal network incorporating information bottleneck (MNIIB).Scientific reports · 2025Article
- A lightweight deep learning model with attention mechanisms for hypertensive retinopathy classification.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.