ArticleEuropean heart journal. Digital health2023
Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.Open heart · 2026Pooled it
- 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
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 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
- Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients.JMIR formative research · 2025Article
- The role of artificial intelligence in cardiovascular research: Fear less and live bolder.European journal of clinical investigation · 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
- 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
- Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review.European heart journal. Digital health · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets.Cardiovascular digital health journal · 2024Article
- Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Characteristics of publicly available digital dementia risk screening tools: A scoping review.Journal of Alzheimer's disease reportsReview
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Authors and funding
18 authors.
Funding
Abstract
Aims: This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD. Methods and results: We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively. Conclusion: The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.
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