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.
Yuji ZhangShusheng XuWenhui XingQiong ChenXu LiuYachuan PuFangran XinHui JiangZongtao YinDengshun TaoDong ZhouYan ZhuBinhang YuanYan JinYuanchen HeYi WuSunny S PoHuishan WangDavid G BendittPublished in: Journal of the American Heart Association (2024)
Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.
Keyphrases
- acute kidney injury
- atrial fibrillation
- cardiac surgery
- artificial intelligence
- catheter ablation
- left atrial
- oral anticoagulants
- left atrial appendage
- heart rate variability
- machine learning
- end stage renal disease
- direct oral anticoagulants
- big data
- heart rate
- deep learning
- heart failure
- newly diagnosed
- ejection fraction
- chronic kidney disease
- emergency department
- primary care
- patients undergoing
- optic nerve
- peritoneal dialysis
- blood pressure
- patient reported outcomes
- coronary artery disease
- clinical evaluation
- patient reported
- left ventricular
- acute coronary syndrome