A view-flexible deep learning framework for automated analysis of 2D echocardiography
0.77 vs. 0.64, LVEF AUC 0.983 vs. 0.968). This approach may broaden the clinical utility of echocardiography by lessening the need for user expertise in image acquisition. Created in BioRender. Malins, J. (2026) https://BioRender.com/lhw4pb1 .
Authors
- Sorin V. Pislaru (ORCID: https://orcid.org/0000-0002-5256-3150)
- Jeffrey G. Malins (ORCID: https://orcid.org/0000-0002-2932-256X)
- Behrouz Rostami (ORCID: https://orcid.org/0000-0002-1091-0388)
- Dan Spiegelstein
- Jeremy J. Thaden (ORCID: https://orcid.org/0000-0003-4489-2349)
- Jwan A. Naser (ORCID: https://orcid.org/0000-0002-6210-7669)
- E S Lee
- Timothy J. Poterucha (ORCID: https://orcid.org/0000-0001-7284-3937)
- John I. Jackson
- Jared Bird
- Jae K. Oh
- Patricia A. Pellikka
- Paul A. Friedman
- D. M. Anisuzzaman
- Talia Amar
- Francisco Lopez-Jimenez
- Zachi I. Attia
- Garvan C. Kane
Institutions
- Mayo Clinic (US)
- Applied Materials (Israel) (IL)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- npj Cardiovascular Health
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1038/s44325-025-00100-7
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00