AI-THEROSCOPE: AI Detection of Subclinical Atherosclerosis From Retinal Images (Atheroscope)

Development and Validation of an AI-Based Tool to Detect Subclinical Atherosclerosis Using Non-Mydriatic Retinal Fundus Images: The AI-THEROSCOPE Project

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Cardiovascular Risk; Subclinical Atherosclerosis

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 884

Contacts and Locations

Study Locations

• Spain
  • Madrid
    • Madrid, Madrid, Spain, 28031
      • Hospital Universitario Infanta Leonor

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Adults undergoing cardiovascular risk assessment at a university hospital cardiovascular risk unit and healthcare workers or relatives participating in cardiovascular screening programs. All participants undergo clinical evaluation, laboratory testing, carotid and femoral vascular ultrasound, and non-mydriatic retinal fundus photography.

Description

Inclusion Criteria:

Adults aged 18 years or older. No previous established cardiovascular disease. Undergoing cardiovascular risk assessment and carotid and femoral vascular ultrasound.

Ability to provide written informed consent.

Exclusion Criteria:

Previous acute coronary syndrome, stroke, or peripheral arterial disease. Previous carotid or femoral vascular surgery or stenting. Previous ophthalmologic surgery. Retinal or ocular diseases that significantly affect retinal vasculature or image quality, including moderate or severe diabetic retinopathy, retinal vascular occlusion, advanced hypertensive retinopathy, exudative age-related macular degeneration, or macular edema.

Inability or unwillingness to provide informed consent.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Participants Undergoing Retinal Imaging and Vascular Ultrasound; Adult participants without previous established cardiovascular disease who undergo standardized cardiovascular risk assessment, laboratory testing, bilateral carotid and femoral vascular ultrasound, and non-mydriatic retinal fundus photography. The cohort is used for the development and validation of an artificial intelligence model for the detection of subclinical atherosclerosis using retinal fundus images. The presence of carotid and/or femoral atherosclerotic plaque assessed by vascular ultrasound serves as the reference standard.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area Under the Receiver Operating Characteristic Curve (AUC) for Detection of Subclinical Atherosclerosis	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.	Baseline

Collaborators and Investigators

• Sponsor: Infanta Leonor University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2023
• Primary Completion (Actual): January 1, 2026
• Study Completion (Estimated): January 1, 2028

Study Registration Dates

• First Submitted: June 1, 2026
• First Submitted That Met QC Criteria: June 1, 2026
• First Posted (Actual): June 4, 2026

Study Record Updates

• Last Update Posted (Actual): June 4, 2026
• Last Update Submitted That Met QC Criteria: June 1, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Deep Learning; Subclinical Atherosclerosis; Carotid Plaque; Retinal Imaging; Non-Mydriatic Fundus Camera; Femoral Plaque; Oculomics
• Other Study ID Numbers: Atheroscope_HUIL

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data (IPD) will not be publicly shared. De-identified data may be made available upon reasonable request to the principal investigator, subject to institutional policies, ethical approval, data protection regulations, and applicable legal requirements.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No