Google and Verily's scientists analyse a medical dataset of almost 300,000 patients.
Scientists from Google and its health-tech subsidiary, Verily, have revealed that analysing scans of the back of an individual's eye can help detect the risk of heart disease. This unique method involves analyzing blood vessels in an area of the eye called the retinal fundus. According to USA Today, by looking at the images, 70% of the time Google's AI was able to accurately predict which patient would experience a heart attack or other major cardiovascular event within five years.
In addition to predicting the various risk factors (age, gender, smoking, blood pressure, etc) from retinal images, our algorithm was fairly accurate at predicting the risk of a CV event directly. As part of this, researchers trained deep learning algorithms using data on 284,335 patients.More news: Anirban ends tied 26th, Watson wins at Riviera
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Deep learning neural networks, which essentially pick apart data similar to our soft human brains, look for patterns in the data and trawl through them to identify indicators of cardiovascular problems. Researchers have known that retinal blood vessels provide a clue to overall cardiovascular health for some years now, and Google is acting on that knowledge.
Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, told The Verge, "They're taking data that's been captured for one clinical reason and getting more out of it than we now do". It opens up a wealth of possibility in regards to preventive care and screening for heart disease. Michael V McConnell, head of Cardiovascular Health Innovations at Verily, said that the research needs more work and a larger patient database to validate these findings before it's ready for clinical testing. 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 the doctor more confidence in the algorithm itself.
"To make this useful for patients, we will be seeking to understand the effects of interventions such as lifestyle changes or medications on our risk predictions and we will be generating new hypotheses and theories to test", Peng said.