Scientists have discovered an incredible way to predict stroke risk through simple eye tests. By analyzing blood vessel patterns in the retina, researchers can now identify potential stroke risks without invasive procedures. The study examined over 45,000 participants and found specific indicators that correlate with stroke probability. This breakthrough could revolutionize early stroke detection and prevention strategies.
"This model presents a practical and easily
implementable approach for incident stroke risk assessment" - CERA
Research Team
Routine eye tests can accurately predict a person's risk of
stroke, according to an international research team on Tuesday.
Key
Points
1. Retinal blood vessel analysis can predict stroke
risk
2. Machine learning identifies 29 key health
indicators
3. Low vascular density increases stroke
probability
4. Non-invasive method for early risk detection
The research, led by the Centre for Eye Research
Australia (CERA) in Melbourne, Australia identified a blood vessel
"fingerprint" at the back of the eye that can be used to predict a
person's stroke risk as accurately as traditional risk factors, but without the
need for invasive tests, Xinhua News agency reported.
The research found that the fingerprint consists of
118 indicators of vascular health and can be analyzed from fundus photography,
a common tool used in routine eye tests.
The team used a machine learning tool called the
Retina-based Microvascular Health Assessment System (RMHAS) to analyse fundus
photos of the eyes of 45,161 people in the UK with an average age of 55.
During an average monitoring period of 12.5 years, 749
participants had a stroke.
The researchers identified 29 of the 118 indicators as
being significantly associated with first-time stroke risk.
Of the 29, about 17 of the indicators were related to
vascular density, the percentage of a region of tissue that is occupied by
blood vessels. Low density in the retina and brain is associated with an
increased risk of stroke.
According to the study, each change in density
indicators was associated with an increased stroke risk of 10-19 per cent.
Decreases in twistedness and complexity indicators
were found to increase stroke risk by 10.5-19.5 per cent.
"Given that age and sex are readily available,
and retinal parameters can be obtained through routine fundus photography, this
model presents a practical and easily implementable approach for incident
stroke risk assessment, particularly for primary healthcare and low-resource
settings," said the researchers, including from Hong Kong.
Stroke affects over 100 million people worldwide and
causes approximately 6.7 million deaths globally every year, the study said,
making early identification of individuals at risk critical to reduce
stroke-related disability and mortality.
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