人工智能启用心电图与大动脉狭窄中的心声图特征之间的相关性
Saki Ito1, Michal Cohen-Shelly1,2, Zachi I Attia1
1Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW Rochester, MN 55905, USA.
人工智能启用心电图 (AI-ECG) 可以通过反映AS严重程度,腹功能障碍和左心室缩来检测大动脉狭窄 (AS). 这种AI-ECG模型可以多因素识别AS,为早期检测提供了一个有前途的工具.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 人工智能启用心电图 (AI-ECG) 显示出在症状出现之前检测大动脉狭窄 (AS) 的前景.
- 影响AI-ECG检测AS的特定心脏功能,结构或血液动力学组成部分仍然不清楚.
研究的目的:
- 在一大群患者中调查AI-ECGAS概率与心声学参数之间的相关性.
- 为了确定AI-ECG在AS检测中反映的潜在心脏特征.
主要方法:
- 应用了梅奥诊所开发的基于卷积神经网络的AI-ECG模型,以识别中度至重度的AS.
- 在102,926名患者中分析了AI-ECG概率和心声回声测量之间的相关性.
- 检查了与患者人口统计和并发症的关联.
主要成果:
- AI-ECG确定了27.7%的患者为AS阳性.
- AI-ECG与大动脉膜面积,峰值速度,平均压力梯度,左心室质量指数,E/e'和左心房体积指数有显著的相关性.
- 年龄与AI-ECG有很强的相关性,类似于与心声回声学参数的相关性.
结论:
- AI-ECG检测AS是多因素的,反映了AS严重程度,扩张功能障碍和左心室缩的组合.
- 人工智能-心电图模型似乎捕获了心脏解剖学和功能特征的分级表示,用于AS识别.
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