Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response
Deep learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR.
Authors
- Hak Seung Lee (ORCID: https://orcid.org/0000-0002-2200-6601)
- Hyoung Seok Lee
- Joo Hee Jeong (ORCID: https://orcid.org/0000-0001-8308-0197)
- Min Sung Lee (ORCID: https://orcid.org/0000-0001-9247-2432)
- Sora Kang (ORCID: https://orcid.org/0000-0002-3847-4442)
- Jaemin Shim (ORCID: https://orcid.org/0000-0001-8251-1522)
- Yun Young Choi (ORCID: https://orcid.org/0000-0002-6228-6636)
- Seong‐Mi Park (ORCID: https://orcid.org/0000-0002-6710-685X)
- Dong‐Hyuk Cho (ORCID: https://orcid.org/0000-0001-8480-9082)
- Jong‐Il Choi (ORCID: https://orcid.org/0000-0001-6617-508X)
- Jeong Min Son (ORCID: https://orcid.org/0000-0001-7744-5735)
- So Ree Kim (ORCID: https://orcid.org/0000-0002-3164-3258)
- Young-Hoon Kim (ORCID: https://orcid.org/0000-0003-4656-0188)
- Mi‐Na Kim (ORCID: https://orcid.org/0000-0001-6589-5122)
- Joon-myung Kwon
- Yun Gi Kim (ORCID: https://orcid.org/0000-0003-2905-6609)
Institutions
- Korea University (KR)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1093/ehjdh/ztae062
- Citations
- 9
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 3.62
Funders
- Korea University
- Korea University Anam Hospital