Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.
Yifeng GaoZhen ZhouBing ZhangSaidi GuoKairui BoShuang LiNan ZhangHui WangGuang YangHeye ZhangTong LiuLei XuPublished in: European radiology (2023)
• A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.
Keyphrases
- deep learning
- electronic health record
- magnetic resonance
- convolutional neural network
- contrast enhanced
- artificial intelligence
- high speed
- clinical decision support
- healthcare
- left ventricular
- magnetic resonance imaging
- big data
- heart failure
- optical coherence tomography
- computed tomography
- adverse drug
- data analysis
- social media
- acute heart failure