Artificial intelligence (AI) can now predict your heart attack risk — by looking into your eyes.
Google scientists are presenting research on an algorithm that can accurately assess a patient’s heart attack risk by examining retinal fundus images.

“Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses,” reads the preamble to a paper published today on Nature Biomedical Engineering.

“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.”

The paper shows that deep learning can extract new knowledge from retinal fundus images and is able to identify cardiovascular risk factors.

These factors include age, gender, smoking status, systolic blood pressure and major adverse cardiac events.

Trained deep-learning models also use anatomical features, such as the optic disc or blood vessels, in predicting a person’s risk.

The model was created using deep-learning models trained on data from 284 335 patients and validated on two independent datasets of 12 026 and 999 patients.

Already, the model has a 70% accuracy rate in predicting whether a person will suffer a cardiac event within five years.