A deep learning-based pipeline for large-scale echocardiography data curation and measurements
This work highlights the potential of deep learning-based automated measurements in echocardiography for data mining in large real-world databases, paving the way for advancements in cardiac imaging research and diagnostics.
Authors
- Håvard Bergesen Dalen (ORCID: https://orcid.org/0009-0003-4000-6298)
- Gilles Van De Vyver (ORCID: https://orcid.org/0009-0006-6458-385X)
- Andreas Østvik (ORCID: https://orcid.org/0000-0003-3895-2683)
- Lasse Løvstakken (ORCID: https://orcid.org/0000-0003-2474-9588)
- Espen Holte (ORCID: https://orcid.org/0000-0002-8891-1372)
- Bjørnar Grenne (ORCID: https://orcid.org/0000-0002-2984-6865)
- David Pasdeloup (ORCID: https://orcid.org/0000-0002-0160-1268)
- Sindre Hellum Olaisen (ORCID: https://orcid.org/0009-0005-7931-4145)
- Jieyu Hu (ORCID: https://orcid.org/0009-0002-9348-9783)
Institutions
- SINTEF (NO)
- Norwegian University of Science and Technology (NO)
- Levanger Hospital (NO)
- St Olav's University Hospital (NO)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1093/ehjdh/ztaf108
- Citations
- 1
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 1.39