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.
In this case, Google'sVerily is using eye scans to accurately predict an individual's age, blood pressure, and whether or not they smoke.
Google researchers along with those from its health technology subsidary Verily Life Sciences, have created a new way with which a person's heart attack risk may be predicted.
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.
The researchers trained the algorithm using machine learning by having it analyze the eye scans and general medical data of around 300,000 patients.
With technological advances in medical and health industry, there are several breakthrough techniques to monitor one's health in a more seamless fashion than what we follow today.
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. We found that each CV risk factor prediction uses a distinct pattern, such as blood vessels for blood pressure, and optic disc for other predictions. This is only slightly worse than the commonly used SCORE method of predicting cardiovascular risk, which requires a blood test and makes correct predictions in the same test 72 percent of the time. The study authors said, "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". Google was surprised by the results and has pushed it further into predicting other CV risk factors. Explaining how the algorithm is making its prediction gives the doctor more confidence in the algorithm itself. In future studies, the researchers said they plan to explore the effects of interventions such as lifestyle changes or medications on risk predictions.