Hospital Universitario Infanta Leonor
Madrid, 28031, Spain
NCT Number: NCT07628088
Cardiovascular risk scores are widely used for risk stratification but may fail to identify a substantial proportion of individuals with subclinical atherosclerosis who are at increased risk of future cardiovascular events. Vascular ultrasound can directly detect carotid and femoral atherosclerotic plaques but its implementation is limited by the need for trained operators and expert interpretation. The AI-THEROSCOPE study aims to develop and validate an artificial intelligence-based tool capable of detecting subclinical atherosclerosis through the analysis of non-mydriatic retinal fundus images. Participants undergo clinical assessment, laboratory testing, carotid and femoral ultrasound, and retinal fundus photography. The performance of the AI model will be evaluated against vascular ultrasound findings as the reference standard for the presence of subclinical atherosclerosis.
This study is active but is not currently recruiting participants.
Notify Me18 year and older
All sexes
Observational
Madrid, 28031, Spain
Cardiovascular disease remains the leading cause of mortality worldwide. Current cardiovascular risk prediction models are useful for population-level risk estimation but may underestimate risk in a substantial proportion of individuals who already have subclinical atherosclerosis. Vascular ultrasound of the carotid and femoral arteries allows direct visualization of atherosclerotic plaques and improves cardiovascular risk stratification, but its widespread use is limited by the requirement for specialized equipment and trained personnel.
Retinal fundus imaging provides a non-invasive assessment of the microvasculature and has emerged as a promising tool for cardiovascular risk evaluation. Recent advances in artificial intelligence and deep learning have demonstrated the ability of retinal image analysis to identify cardiovascular risk factors and predict cardiovascular outcomes.
The AI-THEROSCOPE study is a prospective observational study designed to develop and validate an artificial intelligence model for the detection of subclinical atherosclerosis using non-mydriatic retinal fundus photographs. Adult participants without previous cardiovascular disease undergo standardized clinical evaluation, laboratory testing, carotid and femoral vascular ultrasound, and bilateral retinal fundus photography.
The presence of carotid and/or femoral atherosclerotic plaque assessed by vascular ultrasound serves as the reference standard. Deep learning techniques will be used to train and validate predictive models based on retinal images. Model performance will be evaluated using discrimination metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.
The ultimate objective of the project is to develop a scalable, non-invasive, and easily deployable tool that may facilitate early detection of subclinical atherosclerosis and improve cardiovascular risk stratification in clinical practice and population screening programs.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: Baseline
Diagnostic performance of the artificial intelligence model based on non-mydriatic retinal fundus images for detecting carotid and/or femoral atherosclerotic plaques, using vascular ultrasound as the reference standard.
Infanta Leonor University Hospital
Other
Development and Validation of an AI-Based Tool to Detect Subclinical Atherosclerosis Using Non-Mydriatic Retinal Fundus Images: The AI-THEROSCOPE Project
Acronym: Atheroscope
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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