Google’s software can predict whether you’re at risk of a heart attack by looking at your eyes
Google’s algorithms can predict whether someone is at risk of a heart attack or stroke by looking at the human eye, potentially speeding up the screening and diagnosis of serious illnesses.
By looking at the human eye, Google’s algorithms were able to predict whether someone had high blood pressure or was at risk of a heart attack or stroke, Google researchers said Monday, opening a new opportunity for artificial intelligence in the vast and lucrative global health industry.
The algorithms didn’t outperform existing medical approaches such as blood tests, according to a study of the finding published in the journal Nature Biomedical Engineering. The work needs o be validated and repeated on more people before it gains broader acceptance, several outside physicians said.
But the new approach could build on doctors’ current abilities by providing a tool that people could one day use to quickly and easily screen themselves for health risks that can contribute to heart disease, the leading cause of death worldwide.
“This may be a rapid way for people to screen for risk,” Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email. “Diagnosis is about to get turbo-charged by technology. And one avenue is to empower people with rapid ways to get useful information about their health.”
Google researchers fed images scanned from the retinas of more than 280,000 patients across the United States and United Kingdom into its intricate pattern-recognizing algorithms, known as neural networks. Those scans helped train the networks on which telltale signs tended to indicate long-term health dangers.
Medical professionals today can look for similar signs by using a device to inspect the retina, drawing the patient’s blood or assessing risk factors such as their age, gender, weight and whether they smoke. 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.