Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure
Use of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.
Authors
- Alvin Chen
- Grzegorz Toporek (ORCID: https://orcid.org/0000-0002-2749-187X)
- Robert L. McNamara (ORCID: https://orcid.org/0000-0002-1364-7749)
- Cristiana Baloescu (ORCID: https://orcid.org/0000-0001-7012-1260)
- Shubham Patil (ORCID: https://orcid.org/0000-0003-3114-2450)
- Christopher L. Moore (ORCID: https://orcid.org/0000-0002-6972-2787)
- Balasundar I. Raju (ORCID: https://orcid.org/0000-0003-0998-6147)
- Alexander Varasteh
- Jane Hall
Institutions
- University of Washington (US)
- Washington University in St. Louis (US)
- Yale University (US)
- Inari Agriculture (United States) (US)
- Philips (United States) (US)
- IPS Research (United States) (US)
Publication Details
- Journal
- The Ultrasound Journal
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s13089-024-00391-4
- Citations
- 1
- Primary Topic
- Ultrasound in Clinical Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- Philips Research Americas