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深度学习在创伤放射学中的应用:一个叙事审查

Chi-Tung Cheng¹, Chun-Hsiang Ooyang², Chien-Hung Liao²

¹Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.

Biomedical journal

|April 28, 2024

概括

深度学习 (DL) 增强创伤成像分析,以更快地检测伤害. 进一步开发和验证对于将AI纳入临床实践和改善患者护理至关重要.

科学领域:

医学成像分析分析医学成像分析
医疗保健中的人工智能
创伤诊断创伤诊断创伤诊断

背景情况:

诊断成像对于创伤护理至关重要,指导干预.
深度学习 (DL) 是一个关键的人工智能技术,彻底改变了医学图像分析.
在医学成像中,DL在分类,细分和病变检测方面表现有前途.

研究的目的:

审查用于创伤成像中开发DL算法的基本概念.
在各种创伤成像模式中概述当前DL进展情况.
讨论创伤护理中DL的未来方向和挑战.

主要方法:

关于创伤成像中的DL现有文献的叙事综述.
探索DL在超声波,X射线和CT扫描中的应用.
分析DL在检测特定创伤损伤中的作用.

主要成果:

在FAST扫描中,DL有效检测自由液体,在X射线和CT上进行创伤性发现.
在CT扫描上,DL有助于识别内出血,脊椎骨折和器官损伤.
目前的DL应用涵盖了创伤评估中使用的多种成像模式.

结论:

DL具有显著的潜力,可以提高创伤成像的效率和准确性.
通过联合学习扩展数据集和增强模型可解释性是未来的关键步骤.
多学科合作对于创伤护理中的实用,现实世界的AI解决方案至关重要.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.医学图像医学图像放射学放射学是一门学科.创伤是一个创伤.计算机断层扫描 (CT) 是一种计算机断层扫描.

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