使用深度学习的鼻节律心电图,对潜在的脉上高心脏类型进行分类
Soonil Kwon1, Jangwon Suh2, Eue-Keun Choi3,4
1Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.
Digital health
|October 7, 2024
概括
深度学习可以使用鼻腔节律心电图来分类心房节重入性心力衰竭 (AVNRT) 和隐藏心房节重入性心力衰竭 (AVRT). 这种方法显示了对这些脑膜上性心律障碍的非侵入性诊断的潜力.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 诊断性上脉性心力衰竭 (PSVT) 可能是困难的,特别是当心电图 (ECG) 在鼻节律期间被采取时.
- 鼻腔节律心电图对于在没有预激发的患者中识别特定的PSVT类型的预测价值有限.
- 非侵入性地区分心室节重入性心力衰竭 (AVNRT) 和隐性心室节重入性心力衰竭 (AVRT) 是一个临床挑战.
研究的目的:
- 通过深度学习的鼻腔节律心电图来研究分类AVNRT和隐藏的AVRT的可行性.
- 评估修改后的ResNet-34深度学习模型的性能,以区分这两种常见的脑膜上下心跳.
主要方法:
- 一项回顾性研究分析了确诊AVNRT或隐藏AVRT的患者的心电图.
- 使用了经过修改的ResNet-34深度学习模型,在公共心电图数据库上进行预训练.
- 该模型的性能使用十倍交叉验证进行了评估,并在单独的持久装置上进行了测试.
主要成果:
- 深度学习模型在测试集上实现了接收器操作特征曲线 (AUROC) 下的面积为0.708.
- 预训练模型显著提高了其性能 (AUROC 0.726与0.668没有预训练,p <0.001).
- 在12和6心电图之间没有观察到AUROC的显著差异 (p = 0.265).
结论:
- 从鼻腔节律心电图上对AVNRT和隐藏的AVRT进行深度学习分类是可行的.
- 这种以人工智能为驱动的方法显示出有助于这些心律失常的非侵入性诊断的希望.
- 需要进一步验证,以将这项技术纳入临床实践.
相关概念视频
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