ArticleCardiovascular digital health journal2024
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.
Article in Cardiovascular digital health journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening.BioData mining · 2026Article
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 2026Article
- Construction and effect evaluation of a prediction model for malnutrition risk in patients recovering from stroke.Frontiers in nutrition · 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
- Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.NPJ digital medicine · 2025Article
- Application of Artificial Intelligence to Deliver Healthcare From the Eye.JAMA ophthalmology · 2025Review
- Retinal Microvascular Biomarker Assessment With Automated Algorithm and Semiautomated Software in the Montrachet Dataset.Translational vision science & technology · 2025Article
- 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
- Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning.Frontiers in medicine · 2025Article
- Time series prediction for monitoring cardiovascular health in autistic patients.Frontiers in psychiatry · 2025Article
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Color Fundus Photography and Deep Learning Applications in Alzheimer Disease.Mayo Clinic proceedings. Digital health · 2024Article
- Validation of neuron activation patterns for artificial intelligence models in oculomics.Scientific reports · 2024Article
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
- 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
- Oculomics of lipid metabolism: A scoping review across anterior and posterior segment diseases.Science progressArticle
Corrections and comments
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual's elevated 10-year ASCVD risk score based on retinal images and limited demographic data. Methods: The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual's 10-year ASCVD risk score using the pooled cohort equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% non-Hispanic White, 99.9% diabetic), composed of 18,900 images from 8969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ≥7.5%. Results: In the UK Biobank internal validation dataset, the DL model achieved an area under the receiver operating characteristic curve of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error -0.2%, and mean absolute error 3.1%. Conclusion: This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.
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