一种支持人工智能的心电图算法,用于预测持续性心房的左心房低压区域
Yirao Tao1,2, Deyun Zhang3,4, Chen Tan5
1Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Journal of cardiovascular electrophysiology
|July 26, 2024
概括
使用心电图 (ECG) 的人工智能 (AI) 算法可以预测心房的患者的低压区域 (LVAs). 这种AI模型与现有的临床LVA检测得分相比,显示出更高的准确性.
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:
- 持久性心房动通常与左心房低压区域 (LVAs) 相关.
- 精确识别LVAs对于指导治疗策略至关重要,例如导管切除.
研究的目的:
- 开发和验证一种支持人工智能的心电图 (ECG) 算法,用于预测持久性心房的患者的LVA.
- 将人工智能算法的性能与已建立的临床风险得分 (DR-FLASH和APPLE) 进行比较.
主要方法:
- 使用来自587名持久性心房的患者的12导电图数据构建了一个深度学习算法.
- 人工智能模型的预测性能使用接收机操作特征 (ROC) 曲线,校准曲线和决策曲线分析进行了评估.
- 性能与DR-FLASH和APPLE临床评分进行了比较.
主要成果:
- 基于人工智能的深度学习算法实现了仅使用心电图进行LVA预测的ROC曲线 (AUROC) 下的面积为0.752.
- 一个结合深度学习和临床特征的随机森林模型达到0.759.75的AUROC.
- 为了预测广泛的LVAs,基于ECG的深度学习模型实现了0.775的AUROC,而随机森林模型实现了0.897.
结论:
- 启用人工智能的心电图分析可以准确预测持久心房动患者的LVAs.
- 开发的AI模型,特别是随机森林模型,在LVA预测中显著超过了传统的DR-FLASH和APPLE风险得分.
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