深度学习可以从心电图中预测左心室缩
Hafiz Naderi1,2, Thomas Kaplan1, Stefan van Duijvenboden1,3
1William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK.
概括
一种深度学习模型从心电图中准确地预测左心室缩 (LVH),优于以前的方法. 需要进一步开发各种数据集以确保广泛适用性.
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:
- 左心室缩 (LVH) 是心血管疾病的重要预测因素.
- 以前使用心电图和临床数据进行LVH分类的监督机器学习模型实现了0.85的AUROC,但需要外部验证.
- 外部验证对于评估预测模型的概括性至关重要.
研究的目的:
- 开发一个深度学习 (DL) 模型,以改进心脏磁共振 (CMR) 衍生的LVH的分类.
- 为了外部评估DL模型在波默兰的健康研究 (SHIP) 队列中的表现.
- 评估基于DL的心电图查工具对LVH预测的可行性.
主要方法:
- 开发了一个完全卷积网络DL模型,使用来自48,835名英国生物库参与者的12心电图和临床变量.
- 该模型预测了索引左心室质量 (iLVM),用于重新校准的后勤回归.
- 在训练,验证和测试组中使用接收操作曲线下的面积 (AUROC) 评估性能,并在SHIP队列中外部评估性能.
主要成果:
- 在英国生物银行队列中,DL模型实现了0.97的AUROC,明显超过了以前的方法.
- 在心电图上QRS复合和心室率被确定为LVH的关键预测指标.
- DL模型对SHIP队列 (AUROC 0.78) 显示了适度的概括性,其中的变异归因于临床配置文件,心电图采集和CMR标签.
结论:
- 基于DL的可扩展查工具可用于从心电图预测LVH是可行的.
- 使用更大,更多样化的数据集进行进一步的模型开发是必要的,以提高概括性.
- 在不同人群中,队列特征和数据采集的差异会影响模型性能.
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