Now, an eye scan that predicts heart disease risks

Eyes may soon open a window to a person’s vulnerability to heart problems. Eye scans using deep learning techniques are all that one may need to gauge whether he or she is prone to heart ailments in the near future, according to a study by scientists at Google Research, Verily Life Sciences and Stanford School of Medicine.

The authors of the study, which appeared in the journal Nature Biomedical Engineering , said they would be able to identify new indicators of heart disease risk present in pictures of retinas using Artificial Intelligence.

“This is an early but promising start. If future research pans out, we do hope that the simpler technique of retinal fundus photography could also give additional information about cardiovascular risk non-invasively. But a lot of scientific work remains,” said Lily Peng of Google Research, the corresponding author of the study.

Deep learning can generate accurate models without us having to tell the system what to look for, she said.

“Our approach uses deep learning to directly predict the outcome, and then has the system tell us what it was using to make the prediction through heatmaps. This allows us to draw connections between changes in the human anatomy and disease, akin to how doctors learn to associate signs and symptoms with the diagnosis of a new disease,” Peng told BusinessLine .

“Cardiovascular disease is the leading cause of death globally and there’s a strong body of research that helps us understand what puts people at risk, said Michael McConnell, a cardiology researcher with Verily and a co-author of the study.

Algorithm developed

Earlier, the team had developed an algorithm based on deep learning to diagnose blindness associated with diabetes.

The current screening of cardiovascular risk relies on a variety of variables derived from a patient’s history and blood samples, such as age, gender, smoking status, blood pressure, body mass index, glucose and cholesterol levels.

More often than not, many of these parameters were not readily available. Citing information available from an electronic-health-record based cardiovascular registry maintained by the American College of Cardiology, the scientists said, the data required to calculate the 10-year risk scores were available for less than 30 of the patients.

Applying deep learning to a retinal fundus image can frequently predict these risk factors — from smoking status to blood pressure – as well as predict the occurrence of a future major cardiovascular event on par with current measures, said McConnell in a blog that appeared on the Verily site.

Another significant aspect associated with the proposed screening is that it brings down the prediction time-frame to five years against the 10 years typically associated with clinical risk predictors currently in use.

For the study, the scientists developed deep-learning models using retinal fundus images of nearly three lakh people available from two countries — the UK and the US — and validated them using those from another 13,000 patients.

The authors, however, cautioned that while the system achieved good results, a dataset of less than three lakh patients is still small for Artificial Intelligence.

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