通过心电图像检测左心室缩功能障碍
Veer Sangha1, Arash A Nargesi2,3, Lovedeep S Dhingra2
1Department of Computer Science (V.S., A.K.), Yale University, New Haven, CT.
Circulation
|July 25, 2023
概括
一个新的深度学习模型可以使用心电图来选左心室 (LV) 系统功能障碍. 这种自动化方法提供了可访问的选,特别是在资源有限的环境中.
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 左心室 (LV) 系统功能障碍显著增加心力衰竭和死亡风险.
- 目前对LV功能障碍的心电图查方法因临床可用性而受到限制.
- 已经开发出一种新的深度学习方法,用于使用心电图进行查.
研究的目的:
- 开发和验证一种深度学习算法,用于从心电图片中检测 LV 系统功能障碍.
- 评估模型在不同临床环境和心电图格式中的性能.
- 为了识别暗示LV系统功能障碍的心电图图案.
主要方法:
- 在385,601个心电图和心声图上训练了一个卷积神经网络.
- 该模型在多个医院系统和前性队列中进行了内部和外部验证.
- 使用梯度加权类激活映射进行信号定位.
主要成果:
- 该模型在各种验证集中实现了高分辨率 (AUROC 0.90-0.95).
- 暗示LV功能障碍的心电图发现与这种情况的几率增加了27倍以上.
- 阳性心电图查预测未来发生 LV 缩功能障碍的风险增加了 3. 9 倍.
结论:
- 一个深度学习模型有效地从心电图片中识别了 LV 系统功能障碍.
- 这种方法提供了一个自动化和可访问的 LV 系统功能障碍查策略.
- 这种方法对缺乏资源的医疗保健机构来说特别有前途.
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