Deep Learning Models Predict Cardiovascular Risk Factors from Images of the Eye

The ability to stratify patients by cardiovascular risk is essential for identifying those likely to suffer a heart attack, stroke, or other heart disease in the future. High-risk patients can then take steps to improve their cardiovascular health. Doctors typically take into account a variety of risk factors: demographics such as age, sex and ethnicity; daily behaviors like exercise, smoking status and diet; as well as results from blood pressure and cholesterol tests. As a simple alternative to the traditional patient questionnaire and blood tests, a team of researchers from Google Research and the Stanford School of Medicine have developed deep learning models to predict cardiovascular risk factors from photographs of the back of the retina. Since these retinal fundus images are already collected for diabetic eye disease screening, this initial study suggests that deep learning could uncover additional information that could be further leveraged for preventative health.