Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State

Radhakrishnan, A.; Friedman, S. F.; Khurshid, S.; Ng, K.; Batra, P.; Lubitz, S. A.; Philippakis, A. A.; Uhler, C.

doi:10.1101/2022.05.26.493497

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bioRxiv · 10.1101/2022.05.26.493497

Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State

Radhakrishnan, A.·Friedman, S. F.·Khurshid, S.·Ng, K.·Batra, P.·Lubitz, S. A.·Philippakis, A. A.·Uhler, C.

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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 cardio-vascular 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 provide a framework for integrating distinct diagnostic modalities into a common representation that better characterizes physiologic state.

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Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., Uhler, C.. 2022-05-28. Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State. https://doi.org/10.1101/2022.05.26.493497

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