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基于深度学习和应变分析的亚临床心房动预测,使用心声回声仪进行应变分析

Sung-Hao Huang¹, Ying-Chi Lin², Ling Chen³

¹Division of Cardiology, Department of Internal Medicine, National Yang Ming Chiao Tung University Hospital, Yilan, Taiwan.

Medical & biological engineering & computing

|May 31, 2025

概括

一个新的深度学习框架准确地检测亚临床心房动 (SCAF),也称为心房高频发作 (AHREs),使用心声图. 这种工具有助于早期检测SCAF,并改善心血管事件的预测.

科学领域:

心脏病学心脏病学
人工智能的人工智能
医疗成像医学成像

背景情况:

亚临床心房动 (SCAF) 或心房高频发作 (AHREs),呈现无症状的心率升高,与心血管风险增加有关.
深度学习 (DL) 模型是通过心声回声学建立的,用于心脏功能分析,但它们在AHRE预测中的使用是新的.

研究的目的:

开发和验证一种新的深度学习框架,用于使用心声数据自动检测心房高频发作 (AHREs).
评估框架在分割左心室和分类AHREs方面的表现.

主要方法:

开发了一个深度学习框架,涉及左心脏 (LA) 分段,LA菌株特征提取和AHRE分类.
这项研究分析了117名患者的心声回声图,80%用于开发,20%用于测试.
一个基于变压器的模型整合了患者的特征,用于AHRE预测.

主要成果:

左心室细分实现了高准确度,子系数为0.923 (腔) 和0.741 (壁).
变压器模型在AHRE分类中表现出强的性能,平均AUC为0.815,准确度为0.809,灵敏度为0.800,特异性为0.783,24小时值.

结论:

开发的DL框架为评估AHREs提供了一种可靠的方法.
这种方法显示出早期亚临床心房动检测的巨大潜力,改善了临床决策和患者的治疗结果.

关键词:

房高频发作事件发生率较高.深度学习是一种深度学习.左心房的细分是左心房的细分左心房应变是左心房应变.

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