Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.
Yuki SahashiMilos VukadinovicGrant DuffyDebiao LiSusan C ChengDaniel S BermanDavid OuyangAlan C KwanPublished in: medRxiv : the preprint server for health sciences (2024)
Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.
Keyphrases
- deep learning
- left ventricular
- magnetic resonance
- contrast enhanced
- computed tomography
- pulmonary hypertension
- heart failure
- artificial intelligence
- magnetic resonance imaging
- convolutional neural network
- machine learning
- oxidative stress
- diffusion weighted imaging
- skeletal muscle
- atrial fibrillation
- current status