Google AI device could predict a person's risk of a heart attack
- Author: Santos West Feb 21, 2018,
Feb 21, 2018, 4:22
The software-based method, described in a paper in the Nature journal Biomedical Engineering, can look at scans of the blood vessels at the back of the eye and accurately predict a person's age and blood pressure.
Published study reveals that this novel technique is much accurate for predicting the various cardiovascular diseases with the help of more invasive methods, which include attaching needle in the arms of patient.
In the era of AI and machine learning, doctors are using patterns, generated by algorithms, to recognise diseases.
While the news is definitely encouraging, Google cautions that more research needs to be done. According to the team, they were able to quantify the association between the retinal vessels and cardiovascular risks identified by researchers from previous medical studies. Given the fact that heart diseases is one of the leading causes of deaths in the world, this algorithm could be a big breakthrough.
Currently, when doctors assess risk for cardiovascular disease, they take into account information such as age, sex, smoking, blood pressure and cholesterol levels from a blood test. In the testing phases, the algorithm was able to identify heart conditions 70% of the time, which is a slightly lower rate of success than the longer SCORE process which is correct around 72% of the time.
Last month alone, Amazon made a decision to create their own company health insurance free from "profit-making incentives", while Apple announced their new effort to sync user medical records with their health app on the iPhone as part their new iOS. "We think that the accuracy of this prediction will go up a little bit more as we kind of get more comprehensive data". Maulik Majmudar, associate director of the Healthcare Transformation Lab at Massachusetts General Hospital, called the model "impressive" but noted that the results show how tough it is to make significant improvements in cardiovascular risk prediction.
"This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images", Peng wrote.
"We opened the black box by using attention techniques to look at how the algorithm was making its prediction".