Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope
The CNN model, utilizing a digital stethoscope, offers a noninvasive and scalable method for early detection of individuals with EF ≤40%. This technology has the potential to facilitate early diagnosis and treatment of heart failure, thereby improving patient outcomes.
Authors
- Emileigh Lastowski
- Scott B. Marrus (ORCID: https://orcid.org/0000-0002-8932-214X)
- Caroline Currie (ORCID: https://orcid.org/0009-0002-3867-9592)
- Stefanus Tao Hwa Kieu (ORCID: https://orcid.org/0000-0003-4629-334X)
- Gregg S. Pressman (ORCID: https://orcid.org/0000-0002-0992-1805)
- Edward Kersh
- Rosalie McDonough (ORCID: https://orcid.org/0000-0002-8060-4490)
- Ling Guo (ORCID: https://orcid.org/0000-0001-8394-4338)
- Salima Qamruddin (ORCID: https://orcid.org/0000-0001-9982-6109)
- Preetham Kumar (ORCID: https://orcid.org/0000-0002-0582-5867)
- Evan Friend
- Hussein Al‐Sudani (ORCID: https://orcid.org/0000-0002-2486-8977)
- Deepak Padmanabhan (ORCID: https://orcid.org/0000-0002-9127-9275)
- John Maidens (ORCID: https://orcid.org/0000-0001-8211-7699)
- John Prince (ORCID: https://orcid.org/0000-0002-3203-8754)
- George Mathew
- Subramaniam Venkatraman
Institutions
- Sutter Health (US)
- Thomas Jefferson University Hospital (US)
- Ochsner Medical Center (US)
- Sri Jayadeva Institute of Cardiovascular Sciences and Research (IN)
- Prairie Education and Research Cooperative (US)
- Alcohol Research Group (US)
- MedStar Heart & Vascular Institute (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.jacadv.2025.101619
- Citations
- 8
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 11.11