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Get Free AccessThis 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.
Louisa Fay, Tobias Hepp, Moritz T. Winkelmann, Annette Peters, Margit Heier, Thoralf Niendorf, Tobias Pischon, Beate Endemann, Jeanette Schulz‐Menger, Lilian Krist, Matthias B. Schulze, Andreas Wienke, Nadia Obi, Bernard C Silenou, Berit Lange, Hans‐Ulrich Kauczor, Wolfgang Lieb, Hansjörg Baurecht, Michael Leitzmann, Kira Trares, Hermann Brenner, Karin B. Michels, Stefanie Jaskulski, Henry Völzke, Konstantin Nikolaou, Christopher L. Schlett, Fabian Bamberg, Mario Lescan, Bin Yang, Thomas Küstner, Sergios Gatidis (2025). Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies. , 26(5), DOI: https://doi.org/10.1093/ehjci/jeaf081.
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Type
Article
Year
2025
Authors
31
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1093/ehjci/jeaf081
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