Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection
An AI-based ECG device for HCM can be implemented successfully in a variety of clinical workflows to help identify new patients with HCM. Future study is warranted to assess scalability and comparisons to standard of care.
Authors
- Mahek Shah (ORCID: https://orcid.org/0000-0002-5065-3355)
- James L. Gentry (ORCID: https://orcid.org/0000-0002-7737-7987)
- Dan L. Musat (ORCID: https://orcid.org/0000-0003-2727-1508)
- Michael Kwan (ORCID: https://orcid.org/0000-0001-9677-514X)
- Christopher J. Love (ORCID: https://orcid.org/0000-0002-7989-2376)
- David Huneycutt
- Vivek Y. Reddy (ORCID: https://orcid.org/0000-0002-5638-4993)
- Ethan Carter (ORCID: https://orcid.org/0000-0002-9896-1402)
- Bryan Doherty
- Joshua Lampert (ORCID: https://orcid.org/0000-0003-0081-0356)
- Jorge E. Silva Enciso
Institutions
- Valley Hospital (US)
- UC San Diego Health System (US)
- Mitel (Canada) (CA)
- Centennial Medical Center (US)
- Methodist Healthcare (US)
- Mount Sinai Hospital (US)
- Vibrant Data (United States) (US)
Publication Details
- Journal
- Heart
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1136/heartjnl-2024-325608
- Citations
- 5
- Primary Topic
- Cardiomyopathy and Myosin Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.94