Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head‐to‐Head Comparison With Hospital‐Based Physician ECG Readers

Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head‐to‐Head Comparison With Hospital‐Based Physician ECG Readers

Background Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high‐risk patients is important but labor‐intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development pr...

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Bibliographic Details
Main Authors: Yuji Zhang, Shusheng Xu, Wenhui Xing, Qiong Chen, Xu Liu, Yachuan Pu, Fangran Xin, Hui Jiang, Zongtao Yin, Dengshun Tao, Dong Zhou, Yan Zhu, Binhang Yuan, Yan Jin, Yuanchen He, Yi Wu, Sunny S. Po, Huishan Wang, David G. Benditt
Format: Article
Language:English
Published: Wiley 2024-02-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
artificial intelligence
atrial electrogram
atrial fibrillation
surface ECG
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.123.032100

Internet

https://www.ahajournals.org/doi/10.1161/JAHA.123.032100

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