An international team of researchers on Friday said that by leveraging AI to analyse retinal images for cardiovascular risk assessment, they aim to bridge a crucial gap in early disease detection.

A recent position paper in the Asia-Pacific Journal of Ophthalmology explores the transformative potential of AI in ophthalmology. The work represents a collaboration among researchers from Penn Engineering, Penn Medicine, the University of Michigan Kellogg Eye Center, St John Eye Hospital in Jerusalem, and Gyeongsang National University College of Medicine in Korea.

With fundus photography enabling the visualization of retina at the back of the eye, the potential of AI in providing systemic disease biomarkers is becoming a reality.

When fundus images are of sufficient quantity and quality, it becomes possible to train AI systems to detect elevated HbA1c levels — an important marker for high blood sugar.

A pilot study trained AI models to predict HbA1c levels based on fundus images.

This study evaluated various factors — such as AI model size and architecture, the presence of diabetes, and patient demographics (age and sex) — and their impact on AI performance.

One of the study observations was that biased training samples for an oculomics model, such as a pool of predominantly older patients, can degrade model performance.

The results of the case study highlight the importance of developing trustworthy AI models for assessing cardiovascular risk factors while addressing the challenges and problems that must be overcome prior to clinical adoption, as well as advancing reliable “oculomics” technology.

This method not only enhances our ability to identify at-risk individuals but also holds promise for transforming how we manage chronic conditions such as diabetes. By focusing on practical applications of this technology, we are advancing towards more personalized and preventative healthcare solutions, the authors noted.

(With inputs from IANS)