Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.
Sang Wouk ChoSeungjin BaekSookyeong HanChang Oh KimHyeon Chang KimYumie RheeNamki HongPublished in: Journal of cachexia, sarcopenia and muscle (2024)
A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.
Keyphrases
- community dwelling
- body composition
- computed tomography
- bone mineral density
- dual energy
- image quality
- metabolic syndrome
- postmenopausal women
- contrast enhanced
- positron emission tomography
- deep learning
- cardiovascular events
- resistance training
- magnetic resonance imaging
- skeletal muscle
- multiple sclerosis
- mass spectrometry
- risk factors
- ms ms
- insulin resistance
- cardiovascular disease
- machine learning
- uric acid
- magnetic resonance
- coronary artery disease
- adipose tissue
- convolutional neural network
- pet ct
- high intensity