The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation.
Daniel PipilasSamuel Freesun FriedmanShaan KhurshidPublished in: Current cardiology reports (2023)
Several AI-enabled models have been recently developed which can discriminate AF risk with reasonable accuracy. AI models utilizing the electrocardiogram waveform appear to extract predictive information which is additive beyond traditional clinical risk factors. By identifying individuals at higher risk for AF, AI-based models may improve the efficiency of preventive efforts (e.g., screening, risk factor modification) intended to reduce risk of AF and associated morbidity.
Keyphrases
- artificial intelligence
- atrial fibrillation
- risk factors
- machine learning
- big data
- deep learning
- oral anticoagulants
- heart failure
- catheter ablation
- left atrial appendage
- left atrial
- oxidative stress
- direct oral anticoagulants
- quality improvement
- percutaneous coronary intervention
- mitral valve
- venous thromboembolism
- anti inflammatory