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Google uses AI, deep learning to predict cardiovascular risk from retina scans
20 February 2018, 07:57 | Louis Holland
AI looks into your eyes to predict cardio risk Feb 20 2018
As with all deep learning analysis, neural networks were then used to mine this information for patterns, learning to associate telltale signs in the eye scans with the metrics needed to predict cardiovascular risk (e.g., age and blood pressure).
LEFT: image of the back of the eye showing the macula (dark spot in the middle), optic disc (bright spot at the right), and blood vessels (dark red lines arcing out from the bright spot on the right). One test showed a 70-percent accuracy, for example, in determining which patient among two had experienced a major cardiovascular event following when the retinal image was taken. By studying the appearance algorithms can predict heart disease.
Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, told The Verge, "They're taking data that's been captured for one clinical reason and getting more out of it than we now do". Apart from the retina scans they also recorded the medical data of these individuals.
"AI offers us the potential for new, less invasive tests for heart health - predicting cardiovascular results from retinal images with computer vision - encouraging early results!", Google CEO Sundar Pichai said on Tuesday.
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In a report that appeared on The Verge, Google said using technology such as machine learning and artificial intelligence (AI), the company's software can accurately deduce data, including an individual's age, blood pressure, and whether or not they smoke.
The opportunity to one day readily understand the health of a patient's blood vessels, key to cardiovascular health, with a simple retinal image could lower the barrier to engage in critical conversations on preventive measures to protect against a cardiovascular event. She added that explaining how the algorithm is making its prediction gives the doctor more confidence in the algorithm itself.
For this study the team used the machine to look into the eyes of 284,335 patients. This could help scientists generate more targeted hypotheses and drive a wide range of future research.
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