Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions
Tarig Elhakim, Kelly Trinh, Arian Mansur, Christopher P. Bridge, Dania Daye
Abstract
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.
Topics & Concepts
Composition (language)Clinical PracticeComputer scienceArtificial intelligencePsychological interventionComputed tomographyMachine learningMedical physicsMedicineRadiologyPhysical therapyLinguisticsPhilosophyPsychiatryNutrition and Health in AgingBody Composition Measurement TechniquesRadiomics and Machine Learning in Medical Imaging