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肺伤:基于深度学习的自动化定量可视化.

Nathan Sarkar1, Lei Zhang1, Peter Campbell1

  • 1Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD, 21201, USA.

Emergency radiology
|June 15, 2023
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概括
此摘要是机器生成的。

自动CT体积测量准确量化肺伤 (肺伤指数,或自动LCI). 在创伤患者中,较高的自动LCI与急性呼吸困扰综合征 (ARDS) 和长时间的重症监护室停留的风险增加有关.

关键词:
在ARDS中,我们使用ARDS.人工智能的人工智能是人工智能.肺部伤 肺部伤定量成像技术 定量成像分段化 分段化 分段化 分段化

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 肺部医学 肺部医学
  • 创伤重症监护中心的重症监护中心.

背景情况:

  • 肺是创伤患者的重大损伤.
  • 早期预测急性呼吸困扰综合征 (ARDS) 对患者管理至关重要.
  • 目前用于量化伤的方法可能不够快速或精确.

研究的目的:

  • 训练和验证深度学习模型用于肺伤的自动CT体积测量.
  • 计算肺损伤指数 (自动LCI) 作为肺总体积的百分比.
  • 评估自动LCI与ARDS,ICU停留时间和机械通风持续时间等临床结果之间的关联.

主要方法:

  • 对302名成年创伤患者进行了回顾性分析,这些患者患有肺伤.
  • 使用nnU-Net深度学习模型进行自动细分伤和整个肺部.
  • 执行了多变量回归和Cox比例危险模型,包括自动LCI和临床变量 (氧和,心率,血压).

主要成果:

  • 深度学习模型在细分肺伤方面取得了高准确性 (Dice分数为0.67,ICC分数为0.90).
  • 较高的自动LCI值与ARDS的发展 (p=0.04),更长的ICU停留 (p=0.02) 和增加机械通风需求 (p=0.04) 有关.
  • 包含自动LCI和临床变量的模型显示了ARDS的良好预测性能 (AUC 0.70).

结论:

  • 自动CT体积测量为量化肺提供了一种可靠的方法.
  • 增加自动LCI是ARDS发展和创伤患者资源利用的重要预测因素.
  • 这种人工智能驱动的方法可以帮助早期风险分层和临床决策风险创伤患者.