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Get Free AccessThis study demonstrates the potential of our proposed methodology to uncover latent patterns in ECG data, providing deeper insights into individual heart rhythm patterns and supporting more nuanced AF risk assessment and the overall effectiveness of AF detection and management. By embedding interpretable artificial intelligence in screening tools, we aimed to improve early detection and reduce the clinical burden of AF.
Ryan A. A. Bellfield, Pablo Rendon Hormiga, Iván Olier, Robyn Lotto, Ian Jones, Professor Gregory Lip, Sandra Ortega‐Martorell (2025). AI-driven clustering and visualization of electrocardiogram signals to enhance screening for atrial fibrillation: The supermarket/hypermarket opportunistic screening for atrial fibrillation study. , 6(10), DOI: https://doi.org/10.1016/j.hroo.2025.07.003.
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Type
Article
Year
2025
Authors
7
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.hroo.2025.07.003
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