ArticleBMC medicine2023
Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Review
- Clinical Positioning and Implementation of a Deep-Learning Retinal Biomarker (Reti-CVD) for Cardiovascular Risk Stratification: A Narrative Review.Journal of clinical medicine · 2026Review
- Waist Circumference Modifies the Association Between a Deep Learning-Derived Retinal Biomarker and Coronary Artery Calcium Score in Asymptomatic Adults.Journal of clinical medicine · 2026Article
- Review of breast arterial calcifications for cardiovascular disease risk prediction.NPJ cardiovascular health · 2026Review
- AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.Journal of medical Internet research · 2026Article
- Risk prediction for individual patients and the pitfalls of selecting an optimal prediction model: do not judge a model by its c-statistic.European journal of epidemiology · 2026Article
- The value of retinal microvascular features in predicting risk stratification and long-term prognosis of patients with acute coronary syndrome.BMC ophthalmology · 2026Article
- From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening.BioData mining · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Deep learning-based retinal risk stratification for 10-year atherosclerotic cardiovascular mortality in patients with hypertension and diabetes: a retrospective cohort study.Clinical hypertension · 2026Article
- 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
- Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.NPJ digital medicine · 2025Article
- Retinal Imaging as a Window into Cardiovascular Health: Towards Harnessing Retinal Analytics for Precision Cardiovascular Medicine.Journal of cardiovascular development and disease · 2025Review
- Real-world feasibility, accuracy and acceptability of automated retinal photography and AI-based cardiovascular disease risk assessment in Australian primary care settings: a pragmatic trial.NPJ digital medicine · 2025Article
- Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases.Scientific reports · 2025Article
- The use of artificial intelligence to identify ophthalmic biomarkers in cardiovascular disease and stroke: a narrative review.Sao Paulo medical journal = Revista paulista de medicina · 2025Review
- 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
- Research Hotspots and Prospects of Artificial Intelligence in Cardiovascular Disease: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 7 institutions in 6 countries.
Funding
Abstract
backgroundCurrently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank.
methodsReti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups.
resultsAmong 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3.
conclusionsReti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.
Indexed as
Identifiers
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.