ArticleNature biomedical engineering2021
A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.
Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 2 of them syntheses that pooled it.
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Who cites it
130 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Predictive Modeling of Coronary Artery Disease Using Color Fundus Photography-Based Features of Retinal Vasculature.Ophthalmology and therapy · 2026Article
- Clinical Positioning and Implementation of a Deep-Learning Retinal Biomarker (Reti-CVD) for Cardiovascular Risk Stratification: A Narrative Review.Journal of clinical medicine · 2026Review
- A deep learning algorithm for coronary heart disease prediction based on retinal fundus photographs and optical coherence tomography.BMC medical imaging · 2026Article
- Tracer Techniques in Ophthalmology: Ocular Applications and Systemic Connections.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Hypertension Management: Implications for Quality Improvement and Prevention of End-Organ Damage.Life (Basel, Switzerland) · 2026Review
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- A Three-dimensional Analytical Framework for Retinal Microvasculature Reveals Layer-associated Vulnerability in Development and Neovascular Remodeling.bioRxiv : the preprint server for biology · 2026Article
- Routine blood tests and machine learning identify complications in high myopia.Nature communications · 2026Article
- Empowering genetic discoveries and cardiovascular risk assessment by predicting electrocardiograms from genotype.NPJ digital medicine · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Deep learning-derived retinal biomarker associated with diabetes-related amputation in type 2 diabetes.Frontiers in endocrinology · 2026Observational
- A deep-learning system for the assessment of coronary heart disease risk via scleral photographs.Scientific reports · 2025Article
- Through the eye to the heart: a scoping review of artificial intelligence in retinal imaging for cardiovascular disease assessment.BMC medical informatics and decision making · 2025Article
- Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical modeling.Scientific reports · 2025Article
- From retina to brain: how deep learning closes the gap in silent stroke screening.NPJ digital medicine · 2025Article
- A deep learning system for detecting silent brain infarction and predicting stroke risk.Nature biomedical engineering · 2025Article
- Subclinical Coronary Atherosclerosis and Retinal Optical Coherence Tomography Angiography.JAMA cardiology · 2025Article
- Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population.Heart (British Cardiac Society) · 2025Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.
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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.