深度学习衍生出的心肌应变
Alan C Kwan1, Ernest W Chang2, Ishan Jain1
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
JACC. Cardiovascular imaging
|March 29, 2024
概括
这项研究引入了用于心声回声学的自动化深度学习应变 (DLS) 分析管道. DLS方法提供了一致的,与供应商无关的全球纵向应变 (GLS) 测量,与手工方法相比降低了变化.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 心声应变测量,特别是全球纵向应变 (GLS),依赖于操作者,并表现出显著的中间商变化.
- 这种可变性限制了回顾性研究和使用标准心声回声学进行患者分析的有用性.
- 需要一种自动化的,开源的,与供应商无关的方法来提高GLS测量的准确性和一致性.
研究的目的:
- 开发和验证一个自动化的深度学习 (DLS) 应变分析管道.
- 评估DLS管道在不同患者群体和成像条件中的性能.
- 建立DLS作为GLS评估的可靠和可重复的工具.
主要方法:
- 在传统的GLS测量中评估了观察者间和供应商间的变化.
- 模拟了轮变化对斑点跟踪应变的影响.
- 开发了DLS管道,使用EchoNet-Dynamic的语义细分来计算内心轮的纵向应变.
- 在外部数据集上对传统的GLS进行了DLS验证,并将其应用于心脏缩和粉样性病患者群体.
主要成果:
- 传统的GLS显示了大量的中间供应商 (ICC=0.29) 和观察者间 (ICC=0.63) 的变化.
- 模拟的轮变化在斑点跟踪测量中引发了重大错误.
- 外部验证表明DLS和2DGLS之间存在中度一致 (ICC=0.56,偏差=-3.31%).
- 在患有心脏缩和心脏粉症中适度一致的群体中,DLS显示出显著的差异 (ICC=0.64,偏差=0.57%).
结论:
- 与手动GLS测量相比,开源的DLS管道提供了较少的变化.
- DLS提供了快速,一致的,与供应商无关的定量结果.
- 该方法是公开可用的,在各种成像品质中具有稳定性,适合研究和临床应用.
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