AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Area of Science:

• Ophthalmology and Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiovascular disease (CVD) is a leading global cause of mortality.
• Early CVD risk assessment is crucial for targeted interventions and improved survival.
• Ocular vasculature offers a potential window for CVD risk stratification due to shared physiological traits with the heart and brain.

Purpose of the Study:

• To review advancements in AI-based ocular image analysis for predicting CVD.
• To explore AI's role in identifying CVD risk factors and novel biomarkers.
• To assess AI's potential in replacing traditional CVD assessment methods and predicting CVD events.

Main Methods:

• Comprehensive literature review of AI applications in ocular imaging for CVD prediction.
• Analysis of various ocular imaging modalities: color fundus photography, optical coherence tomography (OCT), OCT angiography, and external eye images.
• Examination of AI's capability to detect subtle ocular features indicative of CVD risk.

Main Results:

• AI integration in ocular imaging overcomes limitations of traditional methods, offering efficiency and accuracy.
• AI can predict CVD risk factors, serve as an alternative to biomarkers like coronary artery calcium scores, and forecast CVD events.
• Novel ocular biomarkers for CVD are being uncovered through AI analysis of retinal vasculature.

Conclusions:

• AI-powered ocular image analysis shows significant promise for non-invasive CVD risk assessment.
• Further research and clinical validation are needed to address limitations and facilitate the translation of AI algorithms into practice.
• AI in ophthalmology represents a transformative approach to cardiovascular health monitoring and early disease detection.