Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.
Frederic CommandeurMarkus GoellerAryabod RazipourSebastien CadetMichaela M HellJacek KwiecinskiXi ChenHyuk-Jae ChangMohamed MarwanStephan AchenbachDaniel S BermanPiotr J SlomkaBalaji K TamarappooDamini DeyPublished in: Radiology. Artificial intelligence (2019)
Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment.© RSNA, 2019See also the commentary by Schoepf and Abadia in this issue.
Keyphrases
- deep learning
- adipose tissue
- risk assessment
- image quality
- dual energy
- artificial intelligence
- convolutional neural network
- computed tomography
- contrast enhanced
- machine learning
- clinical practice
- positron emission tomography
- magnetic resonance imaging
- human health
- heavy metals
- high fat diet
- high throughput
- skeletal muscle
- metabolic syndrome
- sensitive detection