Google to help detect heart disease through retina scan

20 February, 2018, 19:54 | Author: Darrell Lopez
  • AI looks into your eyes to predict cardio risk

In this study, using data from 284,335 patients, the researchers were able to predict cardiovascular risk factors with surprisingly high accuracy.

In addition, while doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, the algorithm could go further to predict the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.

Medical professionals may soon be able to predict heart disease through a non-invasive eye test, thanks to a new artificial intelligence (AI) programme developed by Google and its subsidiary Verily. This unique method involves analyzing blood vessels in an area of the eye called the retinal fundus. When given the retinal image of a patient who experienced a major cardiovascular event up to five years after the image was taken, and the image of a patient who did not, the algorithm could determine which patient experienced the health event 70% of the time.

In the era of AI and machine learning, doctors are using patterns, generated by algorithms, to recognise diseases.

It claims that its technology can evaluate scans of the back of the eye and accurately deduce a person's risk of cardiovascular problems against other data, including their age, blood pressure and gender.

If the eyes are the window to the soul, Google thinks it's proven that the retina can offer a window to the state of the heart. Eye scans and medical data are combined with deep learning analysis, neural networks and then scanned for patterns for cardiovascular risk.

The researchers concluded: "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". Peng said that they expanded their exercise and asked the deep learning model to predict whether a person was a smoker or what their blood pressure was based on retinal images. These techniques allow us to generate a heatmap that shows which pixels were the most important for a predicting a specific CV risk factor. Explaining how the algorithm is making its prediction gives doctor more confidence in the algorithm itself.

According to the researchers, the algorithm that this device uses is quicker than traditional predictor tests using blood tests.



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