深度学习放射学在剪切波弹性学和b模式超声波视频的隔膜断奶结果预测结果的深度学习

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Diagnostic Ultrasound Imaging of Mouse Diaphragm Function

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Changchun Li¹, Yan Liu², Rui Dong²

¹The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; School of Communication and Information Engineering, Shanghai University, Shanghai, China.

Medical engineering & physics

|February 16, 2024

概括

深度学习放射学 (DLR) 准确区分对照组和患者组,并使用隔膜超声波视频预测断奶结果. 这种创新方法增强了隔膜细分和特征提取,以改善临床预测.

科学领域:

医疗成像医学成像
人工智能的人工智能
临界护理医学临界护理医学

背景情况:

隔膜功能障碍在机械通风患者中很常见.
准确评估隔膜功能对于预测断奶结果至关重要.
目前用于隔膜评估的方法可能是主观的,耗时的.

研究的目的:

开发一种自动深度学习放射学 (DLR) 方法,用于使用剪波弹性学 (SWE) 和B模式超声波进行隔膜分析.
在对照组和患者组之间进行分类.
预测机械通风断奶的结果.

主要方法:

利用U-Net进行双模超声波视频 (SWE和B模式) 中的自动隔膜细分.
通过使用统计测试和LASSO提取了高通量放射学特征并应用了尺寸缩小.
在这两个任务中使用支持矢量机 (SVM) 开发了分类模型.

主要成果:

在自动隔膜细分方面获得了87.89%的子得分.
在分类模型中将4524的放射学特征减少到10和20.
在两个分类任务中,接收器运行特征曲线下的获得面积为84.01%和94.37%.

结论:

拟议的DLR方法可以创新地自动对隔膜区域进行细分.
从双模图像中深度挖掘放射学特征证明是有效的.
DLR的方法在预测断奶结果方面表现出有效性.

关键词:

深度学习的放射学学.隔膜图像细分隔膜图像细分高通量放射学特征具有高通量放射学特征.超声波视频超声波视频断奶结果预测预测

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