Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Adityanarayanan Radhakrishnan, Sam F. Friedman, Shaan Khurshid, Kenney Ng, Puneet Batra, Steven A. Lubitz (), Anthony A. Philippakis () and Caroline Uhler ()
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Adityanarayanan Radhakrishnan: Massachusetts Institute of Technology
Sam F. Friedman: Broad Institute of MIT and Harvard
Shaan Khurshid: Broad Institute of MIT and Harvard
Kenney Ng: IBM T.J. Watson Research Center
Puneet Batra: Broad Institute of MIT and Harvard
Steven A. Lubitz: Broad Institute of MIT and Harvard
Anthony A. Philippakis: Broad Institute of MIT and Harvard
Caroline Uhler: Massachusetts Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Date: 2023
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DOI: 10.1038/s41467-023-38125-0

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