Towards standardizing mitral transcatheter edge-to-edge repair with deep-learning algorithm: a comprehensive multi-model strategy
ECHO-PREP demonstrates the feasibility of an integrated AI-assisted workflow for MR assessment, combining quality control, dynamic 4D valve quantification, and scallop-level anatomy interpretation. These results support the potential of AI to standardize M-TEER eligibility, reduce inter-observer variability, and provide decision support across centers with different levels of expertise.
Authors
- Denis Corbin (ORCID: https://orcid.org/0009-0007-4201-440X)
- Thomas Modine (ORCID: https://orcid.org/0000-0003-4169-3766)
- Théo Godefroy
- Olivier Tastet (ORCID: https://orcid.org/0000-0001-5694-6266)
- Frédéric Lesage (ORCID: https://orcid.org/0000-0003-3699-1283)
- Silvia Paola Corona (ORCID: https://orcid.org/0000-0003-4971-1076)
- Walid Ben Ali (ORCID: https://orcid.org/0000-0003-0964-6433)
- Stephan von Bardeleben
Institutions
- Montreal Heart Institute (CA)
- Hôpital Cardiologique du Haut-Lévêque (FR)
- Polytechnique Montréal (CA)
Publication Details
- Journal
- Frontiers in Network Physiology
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/fnetp.2025.1701758
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00