Published: Tue, February 20, 2018
Medical | By Alyssa Little

Google researchers use Eye scan to detect heart risk

Google researchers use Eye scan to detect heart risk

The rear interior wall of the eye (the fundus) is chock-full of blood vessels that reflect the body's overall health.

Comparing the image of the fundus in two patients, one of whom within the next 5 years suffered a heart attack, Google's algorithm learned to determine the risk with an accuracy of 70%. This can then be used to predict cardiac events very accurately. With its system, Google's deep learning tech is able to predict cardiovascular risk in any given individual simply using images of their retina.

"Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses". "They're taking data that's been captured for one clinical reason and getting more out of it than we now do", said Oakden-Rayner. Apart from the retina scans they also recorded the medical data of these individuals. In future studies, the researchers said they plan to explore the effects of interventions such as lifestyle changes or medications on risk predictions.

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Peng also said that Google was able to understand how the algorithm was making its prediction.

"The caveat to this is that it's early, (and) we trained this on a small data set", says Google's Lily Peng, a doctor and lead researcher on the project. Discovering that we could do this is a good first step.

The true power of this kind of technological solution is that it could flag risk with a fast, cheap and noninvasive test that could be administered in a range of settings, letting people know if they should come in for follow-up. According to her, the operational methodology of the algorithm can in future allow Google to generate a heatmap that shows which pixels were the most important elements for a predicting a specific CV risk factor. Typically, this assessment includes examining risk factor such as age, sex, smoking, blood pressure, and cholesterol, as well as taking into account whether the patient has another disease associated with increased risk of cardiovascular issues, such as diabetes.

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For the study, the scientists developed deep learning models using retinal fundus images of almost 3, 00,000 people available from two countries; the United Kingdom and the U.S. and validated them using those from another 13,000 patients. For instance, the algorithm focuses on blood vessels when predicting blood pressure. Google just combined that reality with technology to take the diagnostic potential to another level.

However, if further studies do validate the findings, the use of retinal images could lower the barrier to doctors discussing preventative measures with a patient.

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