Machine learning identifies esophageal luminal temperature patterns associated with thermal injury in catheter ablation for atrial fibrillation.
Yaacoub ChahineTanzina AfrozeSavannah F BifulcoDemyan V TekmenzhiMahbod JafarvandPatrick M BoyleNazem AkoumPublished in: Journal of cardiovascular electrophysiology (2024)
The rate of LET change and AUC for the recorded temperature predicted EDEL, whereas absolute peak temperatures did not.
Keyphrases
- catheter ablation
- atrial fibrillation
- machine learning
- left atrial
- left atrial appendage
- oral anticoagulants
- direct oral anticoagulants
- artificial intelligence
- heart failure
- big data
- percutaneous coronary intervention
- gene expression
- deep learning
- acute coronary syndrome
- dna methylation
- coronary artery disease
- venous thromboembolism