A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.
Bing-Hua ChenChong-Wen WuDong-Aolei AnJi-Lei ZhangYi-Hong ZhangLing-Zhan YuKennedy WatsonLuke WesemannJiani HuWei-Bo ChenJian-Rong XuLei ZhaoChaoLu FengMeng JiangJun PuLian-Ming WuPublished in: European radiology (2023)
• The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.
Keyphrases
- deep learning
- convolutional neural network
- artificial intelligence
- primary care
- machine learning
- physical activity
- magnetic resonance imaging
- optical coherence tomography
- heart failure
- left ventricular
- healthcare
- coronary artery disease
- electronic health record
- cross sectional
- high throughput
- magnetic resonance
- contrast enhanced
- computed tomography
- health information