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Retinal Imaging as a Window into Cardiovascular Health: Towards Harnessing Retinal Analytics for Precision
Jay Bharatsingh Bisen1, Hayden Sikora1, Anushree Aneja1
1Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
Insights
Cardiac-oculomics uses retinal imaging to detect cardiovascular disease (CVD) biomarkers. This field shows promise for early CVD detection and risk prediction, aiding preventive strategies.
Area of Science:
- Ophthalmology
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) poses a significant health burden, driving the need for advanced diagnostic and preventive strategies.
- Retinal imaging traditionally identifies vascular changes in hypertension and diabetes.
- Cardiac-oculomics emerges as a novel field linking retinal biomarkers to various cardiovascular conditions.
Purpose of the Study:
- To review the current literature on cardiac-oculomics and its potential applications in cardiovascular medicine.
- To highlight the role of retinal imaging biomarkers in assessing cardiovascular risk and disease progression.
- To encourage interdisciplinary collaboration between cardiology and ophthalmology.
Main Methods:
- Review of existing scientific literature on cardiac-oculomics.
- Analysis of various retinal imaging modalities: color fundus photography (CFP), optical coherence tomography (OCT), and OCT angiography (OCTA).
- Exploration of artificial intelligence (AI) applications in interpreting imaging-derived cardiovascular biomarkers.
Main Results:
- Retinal imaging biomarkers are associated with the presence, progression, and risk of diverse CVDs (hypertension, carotid artery disease, heart failure, etc.).
- AI models show potential for enhancing CVD risk prediction using retinal data.
- Retinal imaging may offer real-time assessment of cardiovascular status and response to treatment.
Conclusions:
- Cardiac-oculomics presents a promising, non-invasive approach for cardiovascular disease detection and risk stratification.
- Further standardization and clinical validation of AI-driven cardiac-oculomics tools are necessary for clinical integration.
- Interdepartmental collaboration is crucial to advance this field and improve patient outcomes.
Abstract:
Rising morbidity and mortality from cardiovascular disease (CVD) have increased interest in precision and preventive management to reduce long-term sequelae. While retinal imaging has traditionally been recognized for identifying vascular changes in systemic conditions such as hypertension and type 2 diabetes mellitus, a new ophthalmologic field, cardiac-oculomics, has associated retinal biomarker changes with other cardiovascular diseases with retinal manifestations. Several imaging modalities visualize the retina, including color fundus photography (CFP), optical coherence tomography (OCT), and OCT angiography (OCTA), which visualize the retinal surface, the individual retinal layers, and the microvasculature within those layers, respectively. In these modalities, imaging-derived biomarkers can present due to CVD and have been linked to the presence, progression, or risk of developing a range of CVD, including hypertension, carotid artery disease, valvular heart disease, cerebral infarction, atrial fibrillation, and heart failure. Promising artificial intelligence (AI) models have been developed to complement existing risk-prediction tools, but standardization and clinical trials are needed for clinical adoption. Beyond risk estimation, there is growing interest in assessing real-time cardiovascular status to track vascular changes following pharmacotherapy, surgery, or acute decompensation. This review offers an up-to-date assessment of the cardiac-oculomics literature and aims to raise awareness among cardiologists and encourage interdepartmental collaboration.
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