Atrial fibrillation detection performance of an insertable cardiac monitor: Results from an Assert-IQ post-market clinical study and a novel artificial intelligence algorithm
Assert-IQ ICM accurately detects AF and quantifies burden for long-term monitoring. The AI algorithm effectively reduces false positives while maintaining high sensitivity.
Authors
- Dhanunjaya Lakkireddy (ORCID: https://orcid.org/0000-0002-1492-2693)
- Fujian Qu
- Vish Charan
- Ulrika Birgersdotter‐Green (ORCID: https://orcid.org/0000-0002-0473-1826)
- Suneet Mittal (ORCID: https://orcid.org/0000-0002-8003-7254)
- Wenjiao Lin
- Frank Cuoco
- Harish Manyam (ORCID: https://orcid.org/0000-0001-9394-1018)
- Kwangdeok Lee
- George Manoukian
- Lin Feng (ORCID: https://orcid.org/0009-0008-6846-3382)
- Willibaldo Ojeda
- Rakesh Gopinathannair (ORCID: https://orcid.org/0000-0003-4611-3687)
- Frederick T. Han (ORCID: https://orcid.org/0000-0003-3195-6032)
- Dale Yoo (ORCID: https://orcid.org/0000-0002-6336-7016)
- Mohammad-Ali Jazayeri
- Michael S. Katcher
Institutions
- Heart Rhythm Society (US)
- Trinity Health (US)
- Danaher (United States) (US)
- Erlanger Health System (US)
- Salem Hospital (US)
- Angelo State University (US)
- Abbott (United States) (US)
- Shannon Applied Biotechnology Centre (IE)
- Valley Health System (US)
- Michigan Medicine (US)
- University of California San Diego Medical Center (US)
Publication Details
- Journal
- Heart Rhythm O2
- Published
- 2025-11-15
- DOI
- https://doi.org/10.1016/j.hroo.2025.10.021
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00