Scientists have discovered a new way to assess a person’s risk of heart disease using machine learning.
By analysing scans of the back of a patient’s eye, the team from Google and its health-tech subsidiary Verily, can accurately deduce data, including an individual’s age, blood pressure, and whether or not they smoke.
They claim this can be used to predict the risk of suffering a major cardiac event with roughly the same accuracy as current leading methods.
The algorithm potentially makes it quicker to analyse a patient’s cardiovascular risk, as it doesn’t require a blood test. However, the method will need to be tested more thoroughly before it can be used in a clinical setting.
The researchers say: “Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data.”
