High-Throughput Deep Learning Detection of Mitral Regurgitation
In this study, a novel automated pipeline for identifying clinically significant MR from full transthoracic echocardiography studies demonstrated excellent performance across large numbers of studies and across multiple institutions. Such an approach has the potential for automated screening and surveillance of MR.
Authors
- Susan C. Cheng (ORCID: https://orcid.org/0000-0002-4977-036X)
- Grant Duffy
- Miloš Vukadinovic (ORCID: https://orcid.org/0000-0002-0431-1493)
- Amey Vrudhula (ORCID: https://orcid.org/0000-0001-8895-1238)
- David Liang (ORCID: https://orcid.org/0000-0002-7343-7583)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
Institutions
- Cedars-Sinai Medical Center (US)
- University of California, Los Angeles (US)
- Palo Alto University (US)
- Cedars-Sinai Smidt Heart Institute (US)
- Stanford University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Circulation
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1161/circulationaha.124.069047
- Citations
- 41
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 16.48