Atrial Fibrillation Ablation Outcome Prediction with a Machine Learning Fusion Framework Incorporating Cardiac Computed Tomography.
Orod RazeghiRidhima KapoorMahmood I AlhusseiniMuhammad FazalSiyi TangCaroline H RoneyAlbert J RogersAnson LeePaul J WangPaul CloptonDaniel L RubinSanjiv M NarayanSteven NiedererTina BaykanerPublished in: Journal of cardiovascular electrophysiology (2023)
Our machine learning approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF. This article is protected by copyright. All rights reserved.
Keyphrases
- deep learning
- machine learning
- atrial fibrillation
- catheter ablation
- computed tomography
- big data
- artificial intelligence
- left atrial
- dual energy
- convolutional neural network
- positron emission tomography
- left atrial appendage
- oral anticoagulants
- image quality
- contrast enhanced
- direct oral anticoagulants
- heart failure
- magnetic resonance imaging
- case report
- radiofrequency ablation
- electronic health record
- left ventricular
- percutaneous coronary intervention
- high throughput
- type diabetes
- glycemic control
- metabolic syndrome
- magnetic resonance
- mitral valve