Real-World Artificial Intelligence–Based Electrocardiographic Analysis to Diagnose Hypertrophic Cardiomyopathy
Prospective real-world application of the AI-ECG algorithm to detect HCM was associated with a high degree of accuracy, varying with the chosen probability threshold. It also enabled the identification of 5% of patients with no prior HCM diagnosis.
Authors
- Zoran Popović (ORCID: https://orcid.org/0000-0001-5692-2299)
- Andrew Gaballa (ORCID: https://orcid.org/0009-0008-8619-3400)
- Milind Y. Desai (ORCID: https://orcid.org/0000-0003-0010-9797)
- Katy Rutkowski
- Susan Ospina (ORCID: https://orcid.org/0009-0004-7175-6582)
- Sanaa Sultana
- Bo Xu (ORCID: https://orcid.org/0000-0002-2985-7468)
- Maran Thamilarasan
- Shada Jadam (ORCID: https://orcid.org/0009-0005-6175-4775)
- Mohammed Abusafia (ORCID: https://orcid.org/0009-0008-8658-8656)
Institutions
- Cleveland Clinic (US)
Publication Details
- Journal
- JACC. Clinical electrophysiology
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1016/j.jacep.2025.02.024
- Citations
- 10
- Primary Topic
- Cardiomyopathy and Myosin Studies
- Type
- article
- Field-Weighted Citation Impact
- 13.88