Google can now detect heart disease through AI-based retina scan
Scanning the back of an individual’s eye reveals factors that can cause heart disease.tech Updated: Feb 20, 2018 12:22 IST
Using machine learning,scientists from Google and its health-tech subsidiary, Verily, can now detect a person’s risk of getting a heart disease.
This new method scans the back of an individual’s eye to analyse the factors that can cause heart disease, according to a report by The Verge.
Google said that it is using technology such as machine learning and artificial intelligence (AI), the company’s software can accurately deduce data, including an individual’s age, blood pressure, and whether or not they smoke. This can then be used to predict their risk of suffering a major cardiac event, such as a heart attack, with nearly the same accuracy as current methods.
With this in place, doctors can detect a patient’s cardiovascular risk, as it doesn’t require a blood test. However, scientists stated that the method will need to be tested more thoroughly before it can be used in a clinical setting.
Google and Verily’s scientists used machine learning to analyse a medical dataset of nearly 300,000 patients, as per the report. This information included eye scans as well as general medical data. Neural networks were then used research information for patterns, detecting signs in the eye scans with the metrics needed to predict cardiovascular risks, such as age and blood pressure.
Research states that the rear interior wall of the eye (the fundus) is chock-full of blood vessels that reflect the body’s overall health. By studying their appearance with camera and microscope, doctors can infer things like an individual’s blood pressure, age, and whether or not they smoke, all of which are crucial indicators of cardiovascular health.
As part of the research, retinal images of two patients were presented, one of whom suffered a cardiovascular event and one of whom did not. Using the aforementioned technology, Google’s algorithm was able to tell the two apart.
If successful, this technology can significantly enhance the medical field in terms of early detection, primarily through minimum human involvement.