Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.
Xin WangShaan KhurshidSeung Hoan ChoiSamuel Freesun FriedmanLu-Chen WangChristopher ReederJames Paul PirruccelloPulkit SinghEmily S LauRachael VennNate DiamantPaolo Di AchilleAnthony A PhilippakisChristopher D AndersonJennifer E HoPatrick T EllinorPuneet BatraSteven A LubitzPublished in: Circulation. Genomic and precision medicine (2023)
Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.
Keyphrases
- atrial fibrillation
- artificial intelligence
- deep learning
- heart rate variability
- heart rate
- body mass index
- left atrial
- catheter ablation
- oral anticoagulants
- machine learning
- genome wide
- convolutional neural network
- heart failure
- hypertrophic cardiomyopathy
- left atrial appendage
- direct oral anticoagulants
- copy number
- blood pressure
- percutaneous coronary intervention
- physical activity
- left ventricular