AI looks into your eyes to predict cardio risk Feb 20 2018
21 February, 2018, 04:17
In the study, Google researchers used retinal-scan data from almost 300,000 patients to "train" a neural network - an intricate series of algorithms - to detect heart-health risks just by looking at images of a patient's eyes. Using this data, the company's software can predict the risk of cardiovascular diseases such as heart attack or stroke. But no one taught the algorithms what to look for: Instead, the systems taught themselves, by reviewing enough data to learn the patterns often found in the eyes of people at risk.
Alphabet's health division, Verily, has a number of ongoingprojects that leverage technology and data science to improve healthcare.
Krumholz cautioned that an eye scan isn't ready to replace more conventional approaches. All of these factors are important predictors of cardiovascular health.
The tech predicts risk while being noninvasive, fast, and convenient.
Deep learning networks have already been applied to produce algorithms capable of diagnosing diseases like melanoma and blindness caused by diabetes.
Google also made sure to determine how the algorithm was making its prediction.
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Given the retinal image of one patient who later experienced a major cardiovascular event (such as a heart attack) and the image of another patient who did not, the algorithm could pick out the heart patient 70 per cent of the time.
Blood vessels can provide a valuable snapshot of your heart health, revealing clots, constrictions and other abnormalities associated with various cardiovascular diseases and conditions.
This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these problems. She added that explaining how the algorithm is making its prediction gives the doctor more confidence in the algorithm itself. "Our work also suggests avenues of future research into the source of these associations, and whether they can be used to better understand and prevent cardiovascular disease", conclude the authors of the study.
The idea that the hallmarks of disease could be detected through computational analysis has been alluring to engineers.
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