Google's AI Uses Retinal Images to Reveal Cardiovascular Risk

Deep machine learning can extract and quantify several risk factors for cardiovascular disease (CVD) from photographs of the retinal fundus, according to findings published online February 19 in Nature Biomedical Engineering. Traditional risk factors for CVD include age, sex, smoking status, blood pressure, body mass index, and blood glucose and cholesterol levels. However, a major limitation in considering these risk factors is that many people do not know all of their values, particularly serum cholesterol, for which body mass index is sometimes used as a substitute. However, another way to assess CVD risk may be from retinal images, which are easily obtained in an outpatient setting. Retinal anatomy may reveal cardiovascular status through the presence of cholesterol emboli, hypertensive retinopathy, and details of blood vessel caliber, bifurcation and further branching patterns, and tortuosity.