Google is taking iris scans to next level. It’s now working on an algorithm, which it hopes can predict heart disease by looking into our eyes and deducing data by analyzing scans of the back of a patient’s eye. Also Read - New avatar of Google Chrome’s offline dinosaur game: How you can play
A paper describing the work has been published in the Nature journal Biomedical Engineering. The research is being undertaken by some scientists from Google and its subsidiary Verily have analysed scans of the back of a patient’s eye to predict their risk of suffering a heart attack. The scientists believe that this algorithm could make it quicker for doctors to diagnose a patient considering it bars the need for a blood test. Also Read - Best camera phones under Rs 35000 to buy in July 2021: Pixel 4a, Mi 11X, and more
In order to analyse the working of the research, the scientists used machine learning on a medical dataset of almost 300,000 patients, which included eye scans as well as general medical data. Also Read - Timex Helix Smart 2.0 with temperature sensor, heart rate sensor launched: Details here
The importance of the eye in this research is attributed to the rear interior wall of the eye, which is full of blood vessels and can be used to judge a person’s overall health. Diabetes and high blood pressure, for example, can cause changes in the retina.
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 currently do.” “Rather than replacing doctors, it’s trying to extend what we can actually do.”
However, while Google’s algorithms did manage to approach the accuracy of current methods, it was still far from perfect. When presented images of the eyes of two different people – one who suffered a major adverse cardiac event such as a heart attack or stroke within five years of the photo and the other who did not – the algorithms could correctly pick the patient who fell ill 70 percent of the time.