Deep learning for atrioventricular regurgitation diagnosis: an external validation study
In cases where a prediction was generated, the model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation. These findings support the feasibility of AI-assisted echocardiography in diverse populations, while underscoring the need for technical alignment between model requirements and local acquisition practices to ensure real-world applicability.
Authors
- Jeffrey G. Malins (ORCID: https://orcid.org/0000-0002-2932-256X)
- Elad Maor (ORCID: https://orcid.org/0000-0002-5087-0464)
- Lior Fisher (ORCID: https://orcid.org/0000-0002-5285-6244)
- Michal Cohen‐Shelly (ORCID: https://orcid.org/0000-0003-4817-8644)
- Kobi Faierstein (ORCID: https://orcid.org/0009-0003-3538-2889)
- Ehud Raanani
- Yossi Asaf
- Robert Klempfner (ORCID: https://orcid.org/0000-0002-7754-6736)
- Michael Fiman
- Ido Cohen (ORCID: https://orcid.org/0000-0003-4528-8194)
- Karin Sudri (ORCID: https://orcid.org/0000-0002-5118-8619)
- Ehud Schwammenthal
Institutions
- Mayo Clinic (US)
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Alzheimer's Association of Israel (IL)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1093/ehjdh/ztaf078
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00