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
Aperçu de l'étude
Statut
Les conditions
Description détaillée
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.
Type d'étude
Inscription (Estimé)
Contacts et emplacements
Lieux d'étude
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Madrid
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Madrid, Madrid, Espagne, 28031
- Hospital Universitario Infanta Leonor
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Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Méthode d'échantillonnage
Population étudiée
La 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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
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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.
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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Area Under the Receiver Operating Characteristic Curve (AUC) for Detection of Subclinical Atherosclerosis
Délai: Baseline
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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.
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Baseline
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Collaborateurs et enquêteurs
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Réel)
Achèvement primaire (Réel)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Autres numéros d'identification d'étude
- Atheroscope_HUIL
Plan pour les données individuelles des participants (IPD)
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Description du régime IPD
Informations sur les médicaments et les dispositifs, documents d'étude
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