AI-THEROSCOPE: AI Detection of Subclinical Atherosclerosis From Retinal Images

NCT07628088 | Active Not Recruiting

• 1 other identifier: Atheroscope_HUIL
• Study Type: observational
• Target: 884
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Subclinical Atherosclerosis; Cardiovascular Risk

Keywords

Artificial Intelligence; Deep Learning; Retinal Imaging; Non-Mydriatic Fundus Camera; Carotid Plaque; Femoral Plaque; Subclinical Atherosclerosis; Oculomics

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Infanta Leonor University Hospital: lead

Study Sites (1)

Hospital Universitario Infanta Leonor

Madrid, Madrid, 28031, Spain

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Specialist in Internal Medicine

Study Record Dates

• First Submitted: June 1, 2026
• First Posted: June 4, 2026
• Study Start: October 1, 2023
• Primary Completion: January 1, 2026
• Study Completion (Estimated): January 1, 2028
• Last Updated: June 10, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)