Key result
Deep learning algorithms applied to electrocardiogram data and cardiac mapping show potential to improve the accuracy of ex vivo intelligent labeling of atrial fibrillation substrates.
Why the study?
Advances in deep learning applied to electrocardiographic data provide new opportunities to explore ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates.
The application of deep learning to electrocardiographic data provides promising new approaches for the ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates.
Answers are grounded in this paper and cite their sources.
May guide AI tool development for AF mapping; leaves open prospective clinical validation before any practice change.
With the development of computer hardware and the growth of clinical database, tremendous progress has been made in the application of deep learning to electrocardiographic data, which provides new ideas for the ex vivo cardiac electrical mapping of atrial fibrillation (AF) substrates. The AF mechanism and current status of AF substrate research are first summarized. Then, the advantages and limitations of cardiac electrophysiological mapping techniques are analyzed. Finally, the application of deep learning to electrocardiogram (ECG) data is reviewed, the problems with the ex vivo intelligent labeling of an AF substrate and the possible solutions are discussed, an outlook on future development is provided.
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Chang et al. (2023) conducted a review in Atrial Fibrillation. Deep learning and ex vivo cardiac electrical mapping was evaluated. Deep learning algorithms applied to electrocardiogram data and cardiac mapping show potential to improve the accuracy of ex vivo intelligent labeling of atrial fibrillation substrates.
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