Google can now scan your eyes for heart disease

Eye scan of a woman

Eye factors like blood vessels and lines in the ocular disc can give away things like that to the very well-trained eye, and this AI was very well-trained to look for discrepancies of just those sorts. The latest applies machine learning to retinal images to identify the risk factors of cardiovascular disease.

The technology, called retinal fundus photography, appears to be roughly as accurate as traditional methods although more research still needs to be done.

Verily's head of cardiovascular health innovations, Michael McConnell, said it is promising but early research.

Alun Hughes, a professor at University College London thinks Google may be on to something due to a "long history of looking at the retina to predict cardiovascular risk", but he still thinks the algorithm merits further tests to be certain, The Verge reported.

In a paper published in Nature Biomedical Engineering, Google researchers, along with those from Verily Life Sciences and the Stanford School of Medicine, used deep learning algorithms trained on data from 284,335 patients.

"This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images", Peng wrote. The report states: "Most cardiovascular risk calculators use some combination of these parameters to identify patients at risk of experiencing either a major cardiovascular event or cardiac-related mortality within a pre-specified time period, such as ten years".

Google engineers used images of human eyes from over 280,000 people from the U.S. and U.K. The images enabled the AI-powered neural networks to recognize some patterns and detect the telltale signs of future heart disease and stroke risks.

Veirly's software can also look at data such as individual's age, blood pressure, and whether or not they smoke before it can predict outcomes. The algorithm was ultimately able to determine which patients eventually developed an ailment, with 70% accuracy. The green traces are the pixels used to predict the risk factors.

Google's algorithms approached the accuracy of current methods but were far from ideal. One vulnerability of many neutral networks today is that it's often unclear how or why they reached that conclusion - a "black box" problem that could undermine doctors' or patients' trust in the results. In addition, this technique could help generate hypotheses for future scientific investigations into CV risk and the retina.

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