机器学习预测中度大动脉狭窄症中的渐进性亚临床心肌功能障碍
Mayooran Namasivayam1,2,3, Thomas Meredith1,2,3, David W M Muller1,2
1Department of Cardiology, St Vincent's Hospital, Sydney, NSW, Australia.
Frontiers in cardiovascular medicine
|June 16, 2023
概括
人工神经网络在中度大动脉狭窄时准确预测心肌功能障碍,识别峰值梯度和GLS等关键特征进行监测. 这有助于了解疾病的进展和患者的结果.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 中度大动脉狭窄症 (AS) 了解甚少,但与亚临床心肌功能障碍和类似于严重AS的不良结果有关.
- 在中度AS中推动渐进性心肌功能障碍的因素需要进一步阐明.
- 人工神经网络 (ANN) 为临床数据集中的模式识别和风险分层提供了潜力.
研究的目的:
- 调查ANN在预测中度AS中亚临床心肌功能障碍进展中的有用性.
- 使用ANN识别与心肌功能障碍进展相关的重点心声学特征.
主要方法:
- 使用66名中度AS患者的纵向心声图数据开发ANN模型.
- 图像表型包括左心室全球纵向应变 (GLS) 和门狭窄症严重程度分析.
- 使用了两个多层感知子模型:一个预测GLS从基线数据的变化,第二个包含串行心声学数据.
主要成果:
- 在仅从基线数据预测GLS变化时,ANN实现了高准确性 (95%的培训,93%的测试).
- 主要基线预测因素包括峰值梯度,能量损失,GLS和DI<0.25.
- 纳入序列数据的模型显示AUC为0.844,最高特征包括无维指数的变化,基线峰值梯度,能量损耗和基线GLS.
结论:
- 在中度的AS中,ANN可以准确地预测渐进性的亚临床心肌功能障碍.
- 监测AS进展的重要特征包括峰值梯度,无维指数,GLS和能量损失.
- 这些已识别的特征需要在中度AS患者中进行密切评估和监测.
关键词:
这是一种LV功能障碍.大动脉狭窄症 (大动脉狭窄症)超声心电图 (Echocardiography) 是一种超声心电图 (Echocardiography) 是一种超声心电图.机器学习是机器学习.神经网络的神经网络的神经网络更多相关视频
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