ReviewEye (London, England)2024
AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.
Review in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Predictive Modeling of Coronary Artery Disease Using Color Fundus Photography-Based Features of Retinal Vasculature.Ophthalmology and therapy · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Association of retinal microvascular abnormalities with cardiovascular diseases in patients with severe carotid artery stenosis.International journal of medical sciences · 2026Observational
- A deep learning system for detecting silent brain infarction and predicting stroke risk.Nature biomedical engineering · 2025Article
- VascX Models: Deep Ensembles for Retinal Vascular Analysis From Color Fundus Images.Translational vision science & technology · 2025Article
- Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop.Nature reviews. Cardiology · 2025Review
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy.International journal of environmental research and public health · 2024Review
- Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.Journal of personalized medicine · 2024Review
- Deprivation and the location of primary care optometry services in England.Eye (London, England) · 2024Article
- Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.Diabetologia · 2024Review
- Changes of the peripapillary vascular parameters in premature infants without retinopathy of prematurity using U-net segmentation.International journal of ophthalmology · 2024Article
- Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives.Journal of personalized medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.
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What OpenQuestion holds
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.