A Generalizable Deep Learning System for Cardiac MRI
Rohan Shad Cyril Zakka Dhamanpreet Kaur Robyn Fong Ross Warren Filice John Mongan Kimberly Kalianos Nishith Khandwala David Eng Matthew Leipzig Walter Witschey Alejandro de Feria Victor Ferrari Euan Ashley Michael A. Acker Curtis Langlotz William Hiesinger
Cardiac MRI allows for a comprehensive assessment of myocardial structure,
function, and tissue characteristics. Here we describe a foundational vision
system for cardiac MRI, capable of representing the breadth of human
cardiovascular disease and health. Our deep learning model is trained via
self-supervised contrastive learning, by which visual concepts in cine-sequence
cardiac MRI scans are learned from the raw text of the accompanying radiology
reports. We train and evaluate our model on data from four large academic
clinical institutions in the United States. We additionally showcase the
performance of our models on the UK BioBank, and two additional publicly
available external datasets. We explore emergent zero-shot capabilities of our
system, and demonstrate remarkable performance across a range of tasks;
including the problem of left ventricular ejection fraction regression, and the
diagnosis of 35 different conditions such as cardiac amyloidosis and
hypertrophic cardiomyopathy. We show that our deep learning system is capable
of not only understanding the staggering complexity of human cardiovascular
disease, but can be directed towards clinical problems of interest yielding
impressive, clinical grade diagnostic accuracy with a fraction of the training
data typically required for such tasks.