Deep learning can predict cardiovascular events from liver imaging

Our study demonstrates that deep learning applied to liver MRI can predict cardiovascular risk, highlighting the role of the liver as a metabolic indicator of early cardiovascular disease. These findings are significant for clinicians and researchers seeking non-invasive, imaging-based biomarkers for cardiovascular risk stratification, particularly in patients who might not yet exhibit overt symptoms. If validated in prospective studies, this approach could enhance current risk assessment models, allowing for earlier and more personalized interventions in high-risk individuals. However, further validation is necessary before clinical implementation, ensuring broad applicability and integration into existing prevention frameworks.

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Publication Details

Journal
JHEP Reports
Published
2025-04-22
DOI
https://doi.org/10.1016/j.jhepr.2025.101427
Citations
2
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
2.71
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article

Deep learning can predict cardiovascular events from liver imaging

Tom Luedde, Peter W. de Leeuw, Gregory Patrick Veldhuizen, Daniel Truhn et al.
2 citations
JHEP Reports
Liver Disease Diagnosis and Treatment
2.71
article

Deep learning can predict cardiovascular events from liver imaging

Tom Luedde, Peter W. de Leeuw, Gregory Patrick Veldhuizen, Daniel Truhn, Marko van Treeck, Jan Clusmann, Carolin Victoria Schneider, Yazhou Chen, Ali El‐Armouche, Didem Çifçi, Jakob Nikolas Kather, Tim Lenz
article en
2 citations

Abstract

Our study demonstrates that deep learning applied to liver MRI can predict cardiovascular risk, highlighting the role of the liver as a metabolic indicator of early cardiovascular disease. These findings are significant for clinicians and researchers seeking non-invasive, imaging-based biomarkers for cardiovascular risk stratification, particularly in patients who might not yet exhibit overt symptoms. If validated in prospective studies, this approach could enhance current risk assessment models, allowing for earlier and more personalized interventions in high-risk individuals. However, further validation is necessary before clinical implementation, ensuring broad applicability and integration into existing prevention frameworks.

JHEP ReportsVol. 7(8)
Heidelberg University (DE), University Hospital Heidelberg (DE), Maastricht University (NL), University of Chicago (US), Düsseldorf University Hospital (DE), Fresenius (Germany) (DE), National Center for Tumor Diseases (DE), University Hospital Carl Gustav Carus (DE), Heinrich Heine University Düsseldorf (DE), Technische Universität Dresden (DE), RWTH Aachen University (DE)
Openalex Percentile: Top 11%
Liver Disease Diagnosis and Treatment
2.71
DOIOpen Access PDFOpenAlex
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