ArticleJournal of the American Medical Informatics Association : JAMIA2023
Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- 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 Healthcare Practice: Validation, Fairness, and Regulatory Challenges: A Systematic Review.Inquiry : a journal of medical care organization, provision and financingPooled 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
- Review of breast arterial calcifications for cardiovascular disease risk prediction.NPJ cardiovascular health · 2026Review
- Artificial intelligence based retinal imaging for cardiovascular risk and statin guidance in retinal vein occlusion.American journal of preventive cardiology · 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
- Deep learning-derived retinal biomarker associated with diabetes-related amputation in type 2 diabetes.Frontiers in endocrinology · 2026Observational
- AI-powered retinal imaging for early ASCVD risk stratification in a hybrid care model: preliminary findings from SAFER.Frontiers in artificial intelligence · 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
- Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.BMC public health · 2025Article
- Feasibility of a method for measurement of retinal pulse-propagated wave velocity in humans.Microvascular research · 2025Article
- Artificial Intelligence-Based Software as a Medical Device (AI-SaMD): A Systematic Review.Healthcare (Basel, Switzerland) · 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
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Nutriomics and artificial intelligence nutrition obesity cohort (NAINOC): a design paper for a prospective cohort for nutrition and obesity research.Clinical hypertension · 2025Article
- Predicting the Degree of Coronary Artery Stenosis Through Retinal Vascular Characteristics and Minimal Clinical Information.International journal of general medicine · 2025Article
- 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
- Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus.World journal of methodology · 2024Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 4 countries.
Funding
Abstract
objectiveThe potential of using retinal images as a biomarker of cardiovascular disease (CVD) risk has gained significant attention, but regulatory approval of such artificial intelligence (AI) algorithms is lacking. In this regulated pivotal trial, we validated the efficacy of Reti-CVD, an AI-Software as a Medical Device (AI-SaMD), that utilizes retinal images to stratify CVD risk. MATERIALS AND
methodsIn this retrospective study, we used data from the Cardiovascular and Metabolic Diseases Etiology Research Center-High Risk (CMERC-HI) Cohort. Cox proportional hazard model was used to estimate hazard ratio (HR) trend across the 3-tier CVD risk groups (low-, moderate-, and high-risk) according to Reti-CVD in prediction of CVD events. The cardiac computed tomography-measured coronary artery calcium (CAC), carotid intima-media thickness (CIMT), and brachial-ankle pulse wave velocity (baPWV) were compared to Reti-CVD.
resultsA total of 1106 participants were included, with 33 (3.0%) participants experiencing CVD events over 5 years; the Reti-CVD-defined risk groups (low, moderate, and high) were significantly associated with increased CVD risk (HR trend, 2.02; 95% CI, 1.26-3.24). When all variables of Reti-CVD, CAC, CIMT, baPWV, and other traditional risk factors were incorporated into one Cox model, the Reti-CVD risk groups were only significantly associated with increased CVD risk (HR = 2.40 [0.82-7.03] in moderate risk and HR = 3.56 [1.34-9.51] in high risk using low-risk as a reference). DISCUSSION: This regulated pivotal study validated an AI-SaMD, retinal image-based, personalized CVD risk scoring system (Reti-CVD).
conclusionThese results led the Korean regulatory body to authorize Reti-CVD.
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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.