Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography
In this study, an automated pipeline was developed to identify clinically significant TR with excellent performance. With open-source code and weights, this project can serve as the foundation for future prospective evaluation of artificial intelligence-assisted workflows in echocardiography.
Authors
- Alan C. Kwan (ORCID: https://orcid.org/0000-0002-4393-1011)
- Christiane Haeffele (ORCID: https://orcid.org/0000-0002-0176-6628)
- Miloš Vukadinovic (ORCID: https://orcid.org/0000-0002-0431-1493)
- Amey Vrudhula
- David Liang (ORCID: https://orcid.org/0000-0002-7343-7583)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
- Daniel S. Berman (ORCID: https://orcid.org/0000-0002-3793-9578)
- Robert Siegel (ORCID: https://orcid.org/0000-0003-3615-7969)
- Susan Cheng
Institutions
- Cedars-Sinai Medical Center (US)
- University of California, Los Angeles (US)
- Cedars-Sinai Smidt Heart Institute (US)
- Stanford University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JAMA Cardiology
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1001/jamacardio.2025.0498
- Citations
- 20
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 27.76