识别急性大动脉解剖患者使用电心图与卷积神经网络的神经图.
Takuto Arita1, Shinya Suzuki1, Jun Motogi2
1Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
International journal of cardiology. Heart & vasculature
|March 29, 2024
概括
带有心电图 (ECG) 的人工智能 (AI) 显示出对查大动脉剖析 (AD) 的前途. 一个卷积神经网络 (CNN) 模型在高风险患者中实现了35%的积极预测率 (PPR),改善了AD检测.
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:
- 电心电图 (ECG) 与人工智能 (AI) 结合,为大动脉剖析 (AD) 的初始查提供了潜在的可能性.
- 实现基于AI的AD检测的高正预测率 (PPR) 仍然是一个重大挑战.
研究的目的:
- 评估卷积神经网络 (CNN) 模型用于检测大动脉解剖 (AD) 的性能,使用数字12导电图数据.
- 评估不同心电图导向配置和患者风险因素对CNN模型诊断准确度和PPR的影响.
主要方法:
- 一项前性队列研究 (Shinken数据库,2010-2017年,N=19,170) 的回顾性分析,使用数字12ECG.
- 一个CNN模型经过训练和验证,使用五倍交叉验证对八导,单导和双导心电图配置进行了AD检测.
- 包括曲线下的面积 (AUC) 和正预测率 (PPR) 在内的绩效指标在整个队列和在具有提升D-二次数和高血压史的子组中进行了评估.
主要成果:
- 在CNN模型中,用于AD检测的八导心电图的AUC达到0.936.
- 在整体队列中,该模型具有7%的PPR和86%的AD敏感性.
- 将CNN应用于具有D-二元水平≥1μg/dL和高血压史的患者中,增加了PPR至35% (86%的灵敏度).
- 在同一高危子组中,单个V1导电图配置显示出高诊断性能 (AUC:0.933) 和提高PPR至38%.
结论:
- 开发的CNN模型使用心电图数据证明了有效的AD检测,在高风险患者中达到30%以上的PPR,同时保持灵敏度.
- 在CNN框架内,单个V1导电图为AI驱动的AD查提供了一种简化但非常有效的方法.
- 这些发现表明,人工智能驱动的心电图分析可以显著提高大动脉剖析的初始查,特别是在高危人群中.
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