Time to capitalise on artificial intelligence in cardiac electrophysiology
Author:
Funder
British Heart Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10840-024-01803-0.pdf
Reference11 articles.
1. Nagarajan VD, Lee SL, Robertus JL, et al. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J. 2021;42:3904–16.
2. Sau A, Ng FS. The emerging role of artificial intelligence enabled electrocardiograms in healthcare. BMJ Medicine. 2023;2:e000193.
3. Baldazzi G, Orrù M, Viola G, et al. Computer-aided detection of arrhythmogenic sites in post-ischemic ventricular tachycardia. Sci Rep. 2023;13(1):6906. https://doi.org/10.1038/S41598-023-33866-W.
4. Tang S, Razeghi O, Kapoor R, et al. Machine learning-enabled multimodal fusion of intra-atrial and body surface signals in prediction of atrial fibrillation ablation outcomes. Circ Arrhythm Electrophysiol. 2022;15:500–9.
5. Williams SE, Roney CH, Connolly A, et al. OpenEP: a cross-platform electroanatomic mapping data format and analysis platform for electrophysiology research. Front Physiol. 2021;12:160.
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