Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound
Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.
Authors
- Sorin V. Pislaru (ORCID: https://orcid.org/0000-0002-5256-3150)
- Francisco López-Jiménez (ORCID: https://orcid.org/0000-0001-5788-9734)
- Jeffrey G. Malins (ORCID: https://orcid.org/0000-0002-2932-256X)
- Behrouz Rostami (ORCID: https://orcid.org/0000-0002-1091-0388)
- Garvan C. Kane (ORCID: https://orcid.org/0000-0002-4308-6356)
- Paul A. Friedman (ORCID: https://orcid.org/0000-0001-5052-2948)
- Patricia A. Pellikka (ORCID: https://orcid.org/0000-0001-6800-3521)
- Jeremy J. Thaden (ORCID: https://orcid.org/0000-0003-4489-2349)
- Jwan A. Naser (ORCID: https://orcid.org/0000-0002-6210-7669)
- DM Anisuzzaman (ORCID: https://orcid.org/0000-0001-8068-2571)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- John I. Jackson
- Jae K. Oh (ORCID: https://orcid.org/0000-0002-8303-5780)
- Jared Bird
- Grace Greason (ORCID: https://orcid.org/0009-0002-9532-4253)
- Eunjung Lee (ORCID: https://orcid.org/0000-0001-9989-3555)
Institutions
- Mayo Clinic in Arizona (US)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100194
- Citations
- 4
- Primary Topic
- Hemodynamic Monitoring and Therapy
- Type
- article
- Field-Weighted Citation Impact
- 6.02