Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence–Enabled ECGs
An artificial intelligence-enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult-trained models offer good performance, improvements are seen when training pediatric-specific models.
Authors
- Francisco López-Jiménez (ORCID: https://orcid.org/0000-0001-5788-9734)
- Paul A. Friedman (ORCID: https://orcid.org/0000-0001-5052-2948)
- Talha Niaz (ORCID: https://orcid.org/0000-0003-2186-584X)
- Kathryn E. Mangold (ORCID: https://orcid.org/0000-0001-7048-2699)
- Malini Madhavan (ORCID: https://orcid.org/0000-0002-2831-4568)
- Jonathan N. Johnson (ORCID: https://orcid.org/0000-0001-7850-0636)
- Scott Anjewierden (ORCID: https://orcid.org/0000-0001-5542-1599)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Donnchadh O’Sullivan (ORCID: https://orcid.org/0000-0002-9492-8576)
- Jason H. Anderson (ORCID: https://orcid.org/0000-0002-3941-0707)
- Samuel J. Asirvatham (ORCID: https://orcid.org/0000-0001-9835-5536)
- Grace Greason (ORCID: https://orcid.org/0009-0002-9532-4253)
- Benjamin W. Eidem
- Shisheer Havangi Prakash
Institutions
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1161/jaha.124.035201
- Citations
- 6
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 2.41