Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes
Comparison to expert segmentations revealed robust performance of Nick in routine clinical cases with variable pathology, supporting its future integration into clinical workflows.
Authors
- Jens Wetzl (ORCID: https://orcid.org/0000-0002-6484-2130)
- Teodora Chițiboi (ORCID: https://orcid.org/0000-0001-5106-1549)
- Jana Veit
- Clemens Ammann (ORCID: https://orcid.org/0000-0002-5962-4008)
- Yashraj Bhoyroo
- Thomas Hadler (ORCID: https://orcid.org/0000-0002-6151-6858)
- Hadil Saad (ORCID: https://orcid.org/0000-0001-8574-644X)
- Philine Reisdorf
- Christian Geppert
- Jeanette Schulz-Menger
Institutions
- Siemens (Germany) (DE)
- Max Delbrück Center (DE)
- Humboldt-Universität zu Berlin (DE)
- German Centre for Cardiovascular Research (DE)
- Siemens Healthineers (Germany) (DE)
- Helios Hospital Berlin-Buch (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- The International Journal of Cardiovascular Imaging
- Published
- 2025-12-26
- DOI
- https://doi.org/10.1007/s10554-025-03596-3
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Siemens Healthineers