Multi-modal artificial intelligence algorithm for the prediction of left atrial low-voltage areas in atrial fibrillation patient based on sinus rhythm electrocardiogram and clinical characteristics: a retrospective, multicentre study
The multimodal AI algorithm, which integrated ECG images and clinical features, predicted the presence of LVAs with a higher degree of accuracy than ECG alone and the clinical LVA scores.
Authors
- Chen Sabrina Tan (ORCID: https://orcid.org/0000-0001-7604-0824)
- Deyun Zhang (ORCID: https://orcid.org/0000-0002-4041-3083)
- Naidong Pang (ORCID: https://orcid.org/0000-0002-6843-9867)
- Shijia Geng (ORCID: https://orcid.org/0000-0002-9628-8808)
- Shenda Hong (ORCID: https://orcid.org/0000-0001-7521-5127)
- Yirao Tao (ORCID: https://orcid.org/0000-0002-2920-3384)
- Ying Tian (ORCID: https://orcid.org/0000-0001-9521-368X)
- Xingpeng Liu (ORCID: https://orcid.org/0000-0001-6493-7271)
Institutions
- Capital Medical University (CN)
- Peking University (CN)
- Hospital of Hebei Province (CN)
- Hebei Yiling Hospital (CN)
- Hebei General Hospital (CN)
- Beijing Chaoyang Emergency Medical Center (CN)
- Dezhou University (CN)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1093/ehjdh/ztae095
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Natural Science Foundation of China
- Peking University
- Fundamental Research Funds for the Central Universities