Back to search

Article

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

2022-10-13

Abstract excerpt

<h4>Purpose</h4> To create and evaluate the accuracy of an artificial intelligence platform capable of using only retinal fundus images to predict both an individual ’ s overall 10 year Cardiovascular Disease (CVD) risk and the relative contribution of the component risk factors that comprise this risk (CVD-AI). <h4>Methods</h4> The UK Biobank and the US-based AREDS 1 datasets were obtained and used for this study...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
90c0dc95-e52d-55a9-9a4c-62bbc9fe7468
DOI
10.1101/2022.10.12.22281017
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)DOI 10.1101/2022.10.12.22281017
A predictive atlas of disease onset from retinal fundus photographsSemantic proximity
Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.Semantic proximity
The German National Cohort: Ophthalmological Assessment, Baseline Profile and Potential for AI-based Eye ResearchSemantic proximity
A Retinal Oct-Angiography and Cardiovascular STAtus (RASTA) Dataset on Swept-Source Microvascular Imaging for Cardiovascular Risk AssessmentSemantic proximity
How does AI detect diabetic retinopathy from retinal photos? A heatmap analysis of 54 deep learning modelsSemantic proximity
Comparative analysis of quantitative fundus results of the left and right eyes and screening for coronary atherosclerotic disease: A binocular studySemantic proximity
Toward Personalized Diabetic Retinopathy Screening: Deep Learning Fundus Image Analysis and Clinical Risk FactorsSemantic proximity
A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical featuresSemantic proximity
Artificial Intelligence for the Diagnosis of De Novo Diabetic Retinopathy: A Scoping Review of Global EvidenceSemantic proximity
Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.Semantic proximity
Autonomous Screening for Laser Photocoagulation in Fundus Images Using Deep LearningSemantic proximity
Comparing tangible retinal image characteristics with deep learning features reveals their complementarity for gene association and disease predictionSemantic proximity
Select a neighboring publication to make it the new centre.