Artificial Intelligence Closes the Gap in Cardiovascular Risk Detection
By harnessing artificial intelligence (AI) to assess retinal images, an international team aims to enhance cardiovascular risk detection at an early stage as per a paper in the Asia-Pacific Journal of Ophthalmology ().
A recent position 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.
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The retina contains about 120 million rod cells that help with night vision and 6 million cone cells for color vision. #artificialintelligence #ai #retina ’
With fundus photography enabling the visualization of the retina at the back of the eye, the potential of AI in providing systemic disease biomarkers is becoming a reality.
AI-Driven Retinal Imaging for Heart Health
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 economics 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.
Reference:
- Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians - (https:www.sciencedirect.com/science/article/pii/S2162098924000963?via%3Dihub)
Source: IANS