Google Has Developed A Way To Predict Your Risk Of A Heart Attack Just By Looking At Your Eye

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A new system created by Verily and Google AI researchers can use photographs of the retina to predict risk factors for cardiovascular disease.

The system works about as well as presently used predictive methods and is far less invasive.

In a recent study, researchers could see what the artificial intelligence software was paying attention to as it studied the eye.

Your eyes might be the perfect windows into your heart.

At least, they're windows that Google-created artificial intelligence software can use to calculate your risk factors for heart disease.

According to a study recently published in the Nature Biomedical Engineering journal, an AI algorithm created by Google AI and Verily Life Sciences (an Alphabet subsidiary that spun off from Google) can predict whether a patient is likely to suffer a major cardiovascular event like a heart attack or stroke within five years, based on a photo of their retina.

So far, the predictions work about as well as presently accepted methods that are more invasive, according to the study.

Learning to predict heart disease

The fact that disease can be spotted in the retina isn't a surprise. Doctors often spot medical conditions including diabetes, extreme high blood pressure, high cholesterol, and some cancers during eye exams.

To mimic that ability, the Verily and Google researchers trained AI software to identify cardiovascular risks by having the system analyze retina photos and health data from 284,335 patients. Specifically, it looked at retinal fundus images — photos that show blood vessels in the eye.

The retina fundus photographs that the AI software uses to asses cardiovascular disease risk. Poplin et al., Biomedical Engineering, 2018

Known risk factors for cardiovascular disease include age, blood pressure, and gender, among other things. Based on an eye scan, the algorithm was able to predict a person's age to within 3.26 years, smoking status with 71% accuracy, and blood pressure within 11 units of the upper number reported in their measurement.

Because the algorithm was so effective at assessing these factors, the researchers decided to see how well it could predict actual strokes and heart attacks.

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