Geometric deep learning-based coronary wall shear stress estimation from real-world patients
Geometric deep learning enables fast, CFD-free coronary WSS estimation from routine angiography, supporting its potential for large-scale, real-world risk stratification.
Authors
- Umberto Morbiducci (ORCID: https://orcid.org/0000-0002-9854-1619)
- Claudio Chiastra (ORCID: https://orcid.org/0000-0003-2070-6142)
- Bianca Griffo
- Girolamo Mastronuzzi
- David Marlevi
- Andrea Erriquez
- Bernard De Bruyne
- Maurizio Lodi Rizzini
- Alessandro Candreva
- Gianluca Campo
- Diego Gallo
- Marco Laudato
- Carlos Collet
Institutions
- NewYork–Presbyterian Hospital (US)
- Politecnico di Torino (IT)
- Karolinska Institutet (SE)
- University Hospital of Zurich (CH)
- Onze Lieve Vrouwziekenhuis Hospital (BE)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2026-02-25
- DOI
- https://doi.org/10.1016/j.compbiomed.2026.111583
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00