Deep Learning–Derived Right Ventricular Ejection Fraction Predicts Mortality in Patients Undergoing Transcatheter Tricuspid Valve Intervention
Deep learning enabled an unbiased echocardiographic assessment of RV function after TTVI and identified a high-risk group with poor outcomes. These findings are exploratory and require external validation; if confirmed, deep learning-enhanced echocardiography may improve risk stratification and guide personalized follow-up strategies in patients undergoing TTVI.
Authors
- Kai Friedrichs (ORCID: https://orcid.org/0000-0001-9546-3048)
- Vera Fortmeier (ORCID: https://orcid.org/0000-0001-6477-7829)
- Amelie Hesse (ORCID: https://orcid.org/0000-0003-0535-9197)
- Mark Lachmann (ORCID: https://orcid.org/0000-0002-1798-550X)
- Michelle Fett
- Hazem Omran
- Jule Tervooren
- Muhammed Gerçek
- Tanja K. Rudolph
- Gerhard Harmsen
- Béla Péter Merkely
- Shinsuke Yuasa
- Attila Kovács
- Karl-Ludwig Laugwitz
- Michael Joner
- Volker Rudolph
Institutions
- Semmelweis University (HU)
- Okayama University (JP)
- University of Johannesburg (ZA)
- Heart and Diabetes Center North Rhine-Westphalia (DE)
- TUM Klinikum (DE)
- German Centre for Cardiovascular Research (DE)
- Deutsches Herzzentrum München (DE)
- Ruhr University Bochum (DE)
Publication Details
- Journal
- JACC Advances
- Published
- 2026-01-20
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102530
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00