  Traditionally, medical discoveries are made by observing associations and
then designing experiments to test these hypotheses. However, observing and
quantifying associations in images can be difficult because of the wide variety
of features, patterns, colors, values, shapes in real data. In this paper, we
use deep learning, a machine learning technique that learns its own features,
to discover new knowledge from retinal fundus images. Using models trained on
data from 284,335 patients, and validated on two independent datasets of 12,026
and 999 patients, we predict cardiovascular risk factors not previously thought
to be present or quantifiable in retinal images, such as such as age (within
3.26 years), gender (0.97 AUC), smoking status (0.71 AUC), HbA1c (within
1.39%), systolic blood pressure (within 11.23mmHg) as well as major adverse
cardiac events (0.70 AUC). We further show that our models used distinct
aspects of the anatomy to generate each prediction, such as the optic disc or
blood vessels, opening avenues of further research.
