简单的模型与深度学习对比,用于从心电图中检测低射出分数
John Weston Hughes1, Sulaiman Somani2, Pierre Elias3
1Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA.
European heart journal. Digital health
|July 31, 2024
概括
简单的心电图 (ECG) 模型几乎可以像复杂的深度学习模型一样准确地检测左心室缩功能障碍 (LVSD). 这些标准的心电图测量模型为诊断LVSD提供了更容易的临床实施和解释.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 深度学习模型在从心电图 (ECG) 波形检测左心室缩功能障碍 (LVSD) 中显示出高准确度.
- 然而,深度学习模型在临床解释性和广泛部署方面存在挑战.
研究的目的:
- 评估使用标准心电图测量的简单模型是否可以在检测LVSD时达到与深度学习模型相比的准确性.
- 评估简单的基于心电图的模型的临床实用性和可移植性.
主要方法:
- 在一个大型数据集 (40,994) 上训练并验证了各种模型,其中包括来自斯坦福大学医学中心的12导电心电图和心声回声图.
- 包括来自哥伦比亚医学中心和英国生物银行的外部验证数据集.
- 使用接收机操作员特征曲线 (AUC) 下的面积进行性能比较,包括随机森林,后勤回归和深度学习模型.
主要成果:
- 555次测量的随机森林模型实现了AUC为0.92,相当于深度学习模型的AUC为0.94.
- 使用五项测量的物流回归模型显示出高性能 (AUC 0.86),表现优于NT-proBNP.
- 更简单的模型在外部验证站点中显示出更大的可移植性.
结论:
- 简单的心电图模型为LVSD检测提供了对深度学习的有价值的替代方案.
- 这些模型实现了高精度,同时在临床环境中实现和解释更容易.
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