Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow
The DL platform for fully automated real-time GLS measurements was feasible, precise, and time efficient. Real-time DL-based feedback allows operators to optimize images during acquisition, thus improving quality metrics relevant to GLS analyses. Implementing this method in clinical practice could streamline workflow and improve efficiency in the echocardiographic laboratory.
Authors
- Stian Stølen
- Andreas Østvik (ORCID: https://orcid.org/0000-0003-3895-2683)
- Håvard Dalen (ORCID: https://orcid.org/0000-0003-1192-3663)
- Erik Smistad (ORCID: https://orcid.org/0000-0002-7258-4709)
- Lasse Løvstakken (ORCID: https://orcid.org/0000-0003-2474-9588)
- V Holmstrom
- Espen Holte (ORCID: https://orcid.org/0000-0002-8891-1372)
- Bjørnar Grenne (ORCID: https://orcid.org/0000-0002-2984-6865)
Institutions
- SINTEF (NO)
- Norwegian University of Science and Technology (NO)
- Levanger Hospital (NO)
- St Olav's University Hospital (NO)
Publication Details
- Journal
- Journal of the American Society of Echocardiography
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1016/j.echo.2025.08.015
- Citations
- 5
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.37
Funders
- Helse Midt-Norge
- Fakultet for medisin og helsevitenskap, Norges Teknisk-Naturvitenskapelige Universitet