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Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic
Laura Andreea Ghenciu1,2, Mirabela Dima3, Emil Robert Stoicescu4,5,6
1Department of Functional Sciences, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania.
Insights
Oculomics, analyzing retinal images, offers a non-invasive method for early cardiovascular disease (CVD) risk prediction. AI models accurately identify risk factors and events, improving patient outcomes and enabling personalized medicine.
Area of Science:
- Ophthalmology and Cardiology
- Biomedical Engineering
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality, necessitating improved early detection and risk assessment strategies.
- Oculomics leverages retinal microvascular changes, observable via fundus imaging and OCT/OCTA, as non-invasive biomarkers for systemic vascular health.
- Traditional CVD risk assessment methods can be supplemented by advanced imaging analysis for enhanced prediction.
Purpose of the Study:
- To evaluate the efficacy of AI-driven analysis of retinal images for predicting cardiovascular risk factors, events, and metabolic diseases.
- To demonstrate the potential of oculomics as a scalable, non-invasive, and cost-effective tool for cardiovascular risk assessment.
- To highlight the role of retinal imaging biomarkers in personalized medicine and early intervention strategies.
Main Methods:
- Utilizing retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) to capture detailed vascular information.
- Developing and applying artificial intelligence (AI) models for automated analysis of retinal vascular parameters (e.g., caliber, tortuosity, branching patterns).
- Comparing the diagnostic and predictive performance of AI-based oculomics with traditional CVD risk assessment methods.
Main Results:
- AI models demonstrated high accuracy in predicting cardiovascular risk factors and events, with area under the curve (AUC) values ranging from 0.71 to 0.87.
- Sensitivity and specificity for AI-driven predictions ranged from 71% to 89% and 40% to 70%, respectively.
- AI analysis of retinal images showed potential to surpass traditional methods in certain aspects of cardiovascular risk prediction.
Conclusions:
- AI-powered oculomics presents a promising, non-invasive approach for early detection and risk stratification of cardiovascular diseases.
- Retinal imaging analysis can serve as a valuable component of personalized medicine, facilitating timely interventions.
- Further research is needed to standardize protocols and validate these biomarkers across diverse populations for widespread clinical adoption.
Abstract:
Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.
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