Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies

This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.

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Publication Details

Journal
European Heart Journal - Cardiovascular Imaging
Published
2025-03-06
DOI
https://doi.org/10.1093/ehjci/jeaf081
Citations
3
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
4.08
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article

Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies

Margit Heier, Bernard C Silenou, Stefanie Jaskulski, Berit Lange et al.
3 citations
European Heart Journal - Cardiovascular Imaging
Cardiac Imaging and Diagnostics
4.08
article

Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies

Margit Heier, Bernard C Silenou, Stefanie Jaskulski, Berit Lange, Mario Lescan, Henry Völzke, Wolfgang Lieb, Thomas Küstner, Hansjörg Baurecht, Hans‐Ulrich Kauczor, Kira Trares, Fabian Bamberg, Andreas Wienke, Beate Endemann, Matthias Bernd Schulze, Tobias Pischon, Louisa Fay, Jeanette Schulz‐Menger, Moritz T. Winkelmann, Nadia Obi, Karin B. Michels, Hermann Brenner, Sergios Gatidis, Thoralf Niendorf, Lilian Krist, Christopher L. Schlett, Bin Yang, Tobias Hepp, Konstantin Nikolaou, Annette Peters, Michael Leitzmann
article en
3 citations

Abstract

This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.

European Heart Journal - Cardiovascular ImagingVol. 26(5)
University of Stuttgart (DE), Helmholtz Association of German Research Centres (DE), Universität Hamburg (DE), University of Freiburg (DE), Zimmer Biomet (Netherlands) (NL), University of Potsdam (DE), German Cancer Research Center (DE), Max Delbrück Center (DE), Heidelberg University (DE), Universitätsmedizin Greifswald (DE), University Medical Center Freiburg (DE), University Hospital Augsburg (DE), University Hospital Heidelberg (DE), Center for Environmental Health (US), Helmholtz Zentrum München (DE), Christian-Albrechts-Universität zu Kiel (DE), Humboldt-Universität zu Berlin (DE), University Medical Center Hamburg-Eppendorf (DE), Helmholtz Centre for Infection Research (DE), German Centre for Cardiovascular Research (DE), Universitäts-Herzzentrum Freiburg-Bad Krozingen (DE), Stanford Medicine (US), Helios Hospital Berlin-Buch (DE), German Center for Diabetes Research (DE), German Institute of Human Nutrition (DE), University of Regensburg (DE), Martin Luther University Halle-Wittenberg (DE), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE)
Good health and well-being
Openalex Percentile: Top 7%
Cardiac Imaging and Diagnostics
4.08
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