Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.
Lu ZhangZhihan XuBeibei JiangYaping ZhangLingyun WangGeertruida H de BockRozemarijn VliegenthartXueqian XiePublished in: The British journal of radiology (2022)
A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT.
Keyphrases
- contrast enhanced
- machine learning
- atrial fibrillation
- diffusion weighted
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- diffusion weighted imaging
- dual energy
- artificial intelligence
- big data
- oral anticoagulants
- catheter ablation
- left atrial
- left atrial appendage
- genome wide
- percutaneous coronary intervention
- image quality
- venous thromboembolism
- bioinformatics analysis
- left ventricular
- pet ct