Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction
External validation studies such as this one provide a rigorous framework to validate an algorithm's performance. This study demonstrated the algorithm's strong diagnostic accuracy over a geographically diverse, independent set of patients. In this generally unselected population, the algorithm produced a test negative result in 78% of the cases, suggesting potential utility as a rule-out strategy to defer echocardiography when other clinical findings are absent.
Authors
- Jordan B. Strom (ORCID: https://orcid.org/0000-0002-6592-6141)
- Rakesh Barve (ORCID: https://orcid.org/0000-0001-7868-4738)
- R Carter
- P. Kerim Friedman (ORCID: https://orcid.org/0000-0003-0026-2927)
- Jessica Cruz
- B A Steinberg
- Heather M. Alger (ORCID: https://orcid.org/0000-0003-1795-9675)
- Mohan Krishna Ranganathan
- Mikolaj A. Wieczorek (ORCID: https://orcid.org/0000-0002-7879-9727)
- Patrick W. Johnson (ORCID: https://orcid.org/0000-0001-8365-1375)
- Zachi I. Attia
- Kevin J. Ferrick
- Francisco Lopez-Jimenez
- Peter A. Noseworthy
- Konstantinos C. Siontis
- Andrew Krumerman
- Roger DeRaad
- Samir Awasthi
- Jonathan W. Waks
Institutions
- Beth Israel Deaconess Medical Center (US)
- Mayo Clinic (US)
- Montefiore Medical Center (US)
- University of Utah (US)
- DNA Medicine Institute (United States) (US)
- Rapid City Regional Hospital (US)
- Northern Westchester Hospital (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2026-01-16
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102537
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00