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基于机器学习的CT人体成分分析在风险预测和预后评估中的作用:现状与未来方向

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

作者信息

Elhakim Tarig, Trinh Kelly, Mansur Arian, Bridge Christopher, Daye Dania

机构信息

Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.

Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.

出版信息

Diagnostics (Basel). 2023 Mar 3;13(5):968. doi: 10.3390/diagnostics13050968.

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.

摘要

CT身体成分分析已被证明在预测健康方面发挥着重要作用,并且如果在临床上实施,有可能改善患者的治疗结果。人工智能和机器学习的最新进展使得从CT扫描中提取身体成分指标具有高速度和准确性。这些指标可以为术前干预提供参考并指导治疗计划。随着CT身体成分分析朝着广泛的临床应用发展,本综述旨在讨论其在临床实践中的临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f7/10000509/3230d6e12abc/diagnostics-13-00968-g001.jpg

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