Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.
Tarig Sami ElhakimKelly TrinhArian MansurChristopher BridgeDania DayePublished in: Diagnostics (Basel, Switzerland) (2023)
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
Keyphrases
- body composition
- machine learning
- artificial intelligence
- dual energy
- computed tomography
- image quality
- contrast enhanced
- resistance training
- bone mineral density
- high speed
- big data
- clinical practice
- healthcare
- deep learning
- positron emission tomography
- public health
- primary care
- magnetic resonance
- patients undergoing
- mental health
- physical activity
- social media