Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence
Our deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.
Authors
- Miloš Vukadinovic (ORCID: https://orcid.org/0000-0002-0431-1493)
- Yuki Sahashi (ORCID: https://orcid.org/0000-0003-2260-4166)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
- Paul P. Cheng (ORCID: https://orcid.org/0000-0003-3429-2702)
- I-Min Chiu (ORCID: https://orcid.org/0000-0001-6876-8217)
Institutions
- Cedars-Sinai Medical Center (US)
- National Sun Yat-sen University (TW)
- University of California, Los Angeles (US)
- Palo Alto University (US)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- Stanford Medicine (US)
- Cedars-Sinai Smidt Heart Institute (US)
- Stanford University (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1093/ehjdh/ztaf112
- Citations
- 1
- Primary Topic
- Pericarditis and Cardiac Tamponade
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Science and Technology Council
- National Institutes of Health
- National Heart, Lung, and Blood Institute