Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Elhakim, Tarig, Trinh, Kelly, Mansur, Arian, Bridge, Christopher, Daye, Dania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000509/
https://www.ncbi.nlm.nih.gov/pubmed/36900112
http://dx.doi.org/10.3390/diagnostics13050968
Descripción
Sumario: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.

Ejemplares similares