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Updated: Jun 18, 2025

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深度学习在心胸比率计算和心肌大病检测中的深度学习

Jakub Kufel¹, Iga Paszkiewicz², Szymon Kocot³

¹Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyków 14, 40-752 Katowice, Poland.

Journal of clinical medicine

|July 27, 2024

概括

一个新的深度学习算法准确地计算了胸部X射线扫描 (CXR) 上的心胸比率 (CTR),有助于检测心和心周溢出. 这种人工智能工具与人类测量有显著的相关性,为快速的临床查提供了潜力.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
放射学放射学是一门学科.

背景情况:

心胸比率 (CTR) 是评估胸部X射线图 (CXR) 中心壮病和心周溢出的一个关键指标.
准确的CTR计算对于及时诊断和患者管理至关重要.
目前的方法可能耗时或容易引起观察者之间的变化.

研究的目的:

评估深度学习算法的性能,从CXR计算CTR.
评估该算法在识别心肌和心周溢血方面的实用性.
将AI衍生的CTR测量与人类专家评估进行比较.

主要方法:

在1020个随机选择的CXR图像上训练了一个深度学习模型.
人工智能模型对心脏和肺部解剖学进行了细分,以确定CTR.
测量结果与使用标准DICOM查看软件和手动注释得到的测量结果进行了比较.

主要成果:

人工智能模型实现了高绩效指标,与联盟的交叉为88.28% (培训) 和83.06% (验证).
F1分数为90.22% (培训) 和90.67% (验证).
人工智能衍生的CTR,横胸直径 (TTD) 和横心径 (TCD) 与人类测量相比显示出统计学上显著的差异 (p < 0.001).

结论:

深度学习算法显示了与人类测量的显著相关性,用于CTR计算.
人工智能方法在临床环境中显示出作为查工具或咨询支持的潜力,特别是在时间敏感的环境中,如ICU和ER.
在广泛采用之前,建议在临床条件下进一步验证.

关键词:

心脏病巨大的心脏病.心脏与胸部的比率是什么胸部X射线图胸部X射线图卷积神经网络是一种卷积神经网络.细分化细分化的细分化

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