Scientists from Google and its health-tech subsidiary Verily have discovered a new way to assess a person’s risk of heart disease using machine learning. By analyzing scans of the back of a patient’s eye, the company’s software is able to accurately deduce data, including an individual’s age, blood pressure, and whether or not they smoke.

This can then be used to predict their risk of suffering a major cardiac event — such as a heart attack — with roughly the same accuracy as current leading methods. The algorithm potentially makes it quicker and easier for doctors to analyze a patient’s cardiovascular risk, as it doesn’t require a blood test.

But, the method will need to be tested more thoroughly before it can be used in a clinical setting. A paper describing the work was published today in the Nature journal Biomedical Engineering, although the research was also shared before peer review last September.

Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, told The Verge that the work was solid, and shows how AI can help improve existing diagnostic tools. “They’re taking data that’s been captured for one clinical reason and getting more out of it than we currently do,” said Oakden-Rayner.

“Rather than replacing doctors, it’s trying to extend what we can actually do.” To train the algorithm, Google and Verily’s scientists used machine learning to analyze a medical dataset of nearly 300,000 patients. This information included eye scans as well as general medical data. Read more from…

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