Artificial intelligence (AI) has a far reaching grasp. It continues to extend its reach into the healthcare space. One of the areas that its having a profound impact is in the space of early disease detection. Its now changing the way we predict and manage cardiovascular disease (CVD), which is a leading cause of mortality worldwide. Traditional risk factors such as age, sex, and family history are combined with clinical parameters like glucose and cholesterol levels to calculate risk, but these methods fall short in assessing vascular condition directly and efficiently.

AI has paved the way for a more cost-effective tool for CVD risk assessment: ocular imaging. The eye’s microvascular system, particularly the retinal part, shares similarities with the brain and cardiovascular system. Research has shown a strong correlation between the retina and CVD. AI has been used to detect ocular diseases, and now, it is being applied to predict CVD from ocular images.

Despite these advancements, challenges remain showing significant limitations due to population variability. The limited availability of high-quality labelled data for AI model training and validation is also major issue. There is also a need for prospective trials to validate the model’s usefulness and safety in real-world scenarios.

The successful transition of AI-ocular image analysis from theory to real-world application requires addressing various challenges, including conducting cost-benefit analyses of AI models, securing funding sources, establishing information technology infrastructure, fostering social acceptance, managing clinical workflow changes, and navigating regulatory issues.

In conclusion, AI in ocular imaging offers a promising tool for CVD risk assessment. However, further research and collaboration are needed to address the challenges and bring this tool to clinical application.