Google AI Assesses Heart Disease Risk with Eye Scan

Google AI scans retina to predict heart disease risk

Using scans of the eye to build a more general health picture has been used in medical research before - the amount of blood vessels that can be seen via microscopic scans of the back of the eye make it a sound location to gain an insight into the body's general health.

'This paper demonstrates that deep learning applied to a retinal fundus image, a photograph that includes the blood vessels of the eye, can frequently predict these risk factors - from smoking status to blood pressure - as well as predict the occurrence of a future major cardiovascular event on par with current measures'.

The tech giant says its method is as reliable as a doctor conducting a blood test, according to a study published Monday in the journal Nature Biomedical Engineering.

Researchers at Verily reveal that their AI based eye-scanning has a 70 percent accuracy in predicting cardiovascular risk.

She told USA Today: 'It's not just when it's going to be used, but how it's going to be used.

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"We look forward to developing and testing our algorithm on larger and more comprehensive datasets". For example, in making predictions about blood pressure, Google's algorithm paid more attention to blood vessels. Via neural networks, it then made connections between disparate data points-including between eye scans and the biggest risk factors for cardiovascular disease.

"While doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, our algorithm could go further to predict the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure", lead author Lily Peng wrote in a Google blog. Unlike prevalent preventive tests involving various blood reports, the new method uses a simple eye-scan to determine the patient's underlying heart conditions.

'I am very excited about what this means for discovery.

"They're taking data that's been captured for one clinical reason and getting more out of it than we now do", Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, told The Verge.


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