Unlocking the Diagnostic Potential of ECG through Knowledge Transfer
  from Cardiac MRI
Özgün Turgut Philip Müller Paul Hager Suprosanna Shit Sophie Starck Martin J. Menten Eimo Martens Daniel Rueckert
The electrocardiogram (ECG) is a widely available diagnostic tool that allows
for a cost-effective and fast assessment of the cardiovascular health. However,
more detailed examination with expensive cardiac magnetic resonance (CMR)
imaging is often preferred for the diagnosis of cardiovascular diseases. While
providing detailed visualization of the cardiac anatomy, CMR imaging is not
widely available due to long scan times and high costs. To address this issue,
we propose the first self-supervised contrastive approach that transfers
domain-specific information from CMR images to ECG embeddings. Our approach
combines multimodal contrastive learning with masked data modeling to enable
holistic cardiac screening solely from ECG data. In extensive experiments using
data from 40,044 UK Biobank subjects, we demonstrate the utility and
generalizability of our method. We predict the subject-specific risk of various
cardiovascular diseases and determine distinct cardiac phenotypes solely from
ECG data. In a qualitative analysis, we demonstrate that our learned ECG
embeddings incorporate information from CMR image regions of interest. We make
our entire pipeline publicly available, including the source code and
pre-trained model weights.