Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Özgün Turgut¹, Philip Müller², Paul Hager², Suprosanna Shit³, Sophie Starck², Martin J Menten⁴, Eimo Martens⁵, Daniel Rueckert⁶

¹School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany. Electronic address: oezguen.turgut@tum.de.

²School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany.

³Department of Quantitative Biomedicine, University of Zurich, Switzerland.

⁴School of Computation, Information and Technology, Technical University of Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Department of Computing, Imperial College London, United Kingdom.

⁵School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany.

⁶School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Department of Computing, Imperial College London, United Kingdom.

PubMed DOI

Abstract

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classify and spatially localise CVD is limited. In contrast, cardiac magnetic resonance (CMR) imaging provides detailed structural information of the heart a…

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