New Retinal Biomarkers for Cardiovascular Diseases: Analysis of Retinal Imaging Based On Deep Learning Algorithms

il y a 3 jours

Dijon, France Laboratoire ICMUBUniversité Bourgogne Europe Temps plein

**New retinal biomarkers for cardiovascular diseases: analysis of retinal imaging based on deep learning algorithms**:

- Réf **ABG-131455**
- Sujet de Thèse
- 25/04/2025
- Financement public/privé
- Laboratoire ICMUB/Université Bourgogne Europe
- Lieu de travail- DIJON - Bourgogne-Franche-Comté - France
- Intitulé du sujet- New retinal biomarkers for cardiovascular diseases: analysis of retinal imaging based on deep learning algorithms
- Champs scientifiques- Sciences de l’ingénieur
- Santé, médecine humaine, vétérinaire

**Description du sujet**:

- his PhD project falls within the field of **oculomics**, an emerging and innovative area of research that leverages retinal imaging to identify ocular biomarkers reflecting systemic diseases. Thanks to recent advances in high-resolution imaging techniques—such as **retinography**, **optical coherence tomography (OCT)**, and **OCT angiography (OCT-A)**—it is now possible to observe the retinal microcirculation in vivo, thereby providing direct access to key vascular biomarkers.
- This approach offers a unique opportunity to study the correlations between retinal microvascularization and systemic vascular pathologies, particularly **neurocardiovascular diseases**. Currently, the assessment of vascular disease risk relies on score-based calculations derived from clinical data (cardiovascular risk factors, family history, lifestyle). These clinical data are used to build risk scales (such as the **Framingham Score** or **HeartScore**). Oculomics could complement these tools by incorporating **individual vascular data**, thereby refining risk scores and contributing to a more personalized approach to medicine.
- Oculomics sits at the intersection of **ophthalmology**, **artificial intelligence**, and **medical specialties** such as **cardiology** and **neurology**. It relies on image analysis strategies and **deep learning algorithms** to fully harness the potential of retinal vascular data by automating the detection of subtle pathological vascular signatures. This paves the way for **early detection** of neurocardiovascular diseases, improved **individualized cardiovascular risk assessment**, and better prediction of vascular recurrence (e.g., **myocardial infarction**, **stroke**).
- This project will be based on existing and well-structured datasets: retinal imaging and rich neurocardiovascular clinical data from the **Montrachet Cohort** and the open-source **RASTA** database. A second phase of the project will involve the implementation of a **prospective longitudinal cohort (BioRetDL project)**, which will include patients post-myocardial infarction and post-stroke (250 patients in each group, with a 2-year follow-up) at the University Hospital of Dijon (CHU de Dijon).

**Prise de fonction**:

- 01/10/2025

**Nature du financement**:

- Financement public/privé

**Précisions sur le financement**:
**Présentation établissement et labo d'accueil**:

- Laboratoire ICMUB/Université Bourgogne Europe

skill and competencies in medical imaging/python/deep learning

Fluent in english- 15/05/2025

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