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A recent study has unveiled that a simple digital photograph of the retina can accurately predict the likelihood of experiencing a heart attack or stroke within the next ten years, achieving an impressive accuracy rate of 70%. This research, backed by the British Heart Foundation and the National Institute for Health and Care Research (NIHR), suggests that routine eye scans could become a valuable tool in monitoring cardiovascular health.
The innovative study highlights the potential of retinal scans to not only assess current heart health but also track changes in risk factors over time. Researchers discovered a correlation between fluctuations in risk scores over a three-year period and the probability of facing a significant cardiovascular event.
Utilizing advanced artificial intelligence (AI), the analysis of eye scans is performed in mere seconds, providing personalized risk assessments. Individuals identified as being at high risk may be referred to general practitioners for further evaluation, possibly leading to the prescription of medications such as blood pressure drugs or statins to manage cholesterol levels effectively. In the future, it is envisioned that individuals undergoing routine eye exams could receive instant notifications regarding their heart health via smartphone alerts.
Dr. Ify Mordi, who spearheaded the study at the University of Dundee, emphasized the critical connection between ocular health and cardiovascular conditions. He noted that damage or constriction of blood vessels observed at the back of the eye often indicates similar issues in blood vessels supplying the heart, increasing the risk of a heart attack or stroke.
This rapid, easy-to-perform scan could complement traditional cardiovascular assessments, such as blood pressure and cholesterol checks, to identify individuals who may benefit from lifestyle modifications or medication.
The research team at the University of Dundee has developed AI technology specifically designed to analyze digital retinal images typically captured during routine eye examinations. Initially, the AI was programmed to detect warning signs, including blood vessel narrowing and blockages. Subsequently, a deep-learning model was employed, allowing the technology to autonomously identify various features in the images, such as the size and arrangement of blood vessels.
After training the AI on approximately 4,200 retinal images, the researchers evaluated its predictive capabilities regarding cardiovascular events by testing it on eye scans from over 1,200 individuals. The AI demonstrated a 70% accuracy rate in predicting those who would experience a heart attack, stroke, or succumb to cardiovascular disease within the following decade.
Additionally, the study involved participants who underwent a follow-up retinal scan three years later. The researchers found that individuals whose AI-generated risk scores increased by as little as 3% during this period faced a 54% heightened risk of significant cardiovascular events compared to their peers.
When comparing the AI's predictions with traditional cardiovascular risk assessments, which take into account factors such as age, sex, blood pressure, cholesterol levels, and smoking status, it was discovered that both methods identified nearly the same proportion of individuals at risk. However, when combining the AI analysis with traditional risk assessment and genetic testing, accuracy improved to 73%, potentially identifying an additional three at-risk individuals per 100.
This AI tool was primarily tested on individuals with diabetes, who routinely undergo retinal scans on the National Health Service (NHS) to detect eye-related complications. Nevertheless, the researchers assert that evaluating the blood vessels in the eye to assess cardiovascular risk is applicable to a broader population.
Professor Bryan Williams, a leading figure at the British Heart Foundation, highlighted the importance of accurately identifying individuals at risk of heart attacks or strokes. He noted that integrating cutting-edge technologies like retinal scans into routine health assessments could enhance risk predictions, aligning with the British Heart Foundation's objective of preventing 125,000 heart attacks and strokes across the UK by 2035. However, further research is essential to ensure the reliability of these predictive methods and to explore the practical implementation of retinal scans in clinical settings.
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