人工智能分析用于从12导电心电图中估计射出分数
Alina Devkota1, Rukesh Prajapati2, Amr El-Wakeel2
1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, USA. ad00139@mix.wvu.edu.
深度学习模型可以从心电图 (ECG) 信号中估计心脏喷射分数 (EF),为心力衰竭 (HF) 诊断提供一个具有成本效益的解决方案. 这项研究验证了农村人口的AI性能,达到0.86.8的AUROC.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 心力衰竭 (HF) 是心血管死亡的主要原因,患病率正在上升.
- 射出分数 (EF) 测量对于HF诊断和管理至关重要.
- 心声图是黄金标准,但由于成本和可访问性受到限制,与心电图 (ECG) 不同.
研究的目的:
- 探索使用机器学习 (ML) 和深度学习 (DL) 模型估计EF的12导电图信号的潜力.
- 评估人工智能模型在代表性不足的阿巴拉契亚农村人口中用于EF估计的性能.
- 评估不同的人口结构对AI公平性和心血管健康准确性的影响.
主要方法:
- 利用了来自西弗吉尼亚州的55500名患者的12导电图数据集.
- 应用了一系列人工智能算法,包括基于随机森林和变压器的DL模型,用于EF估计.
- 分析了使用各种值,单线或多线心电图信号的模型性能,并进行了可解释性分析.
主要成果:
- 深度学习算法实现了最高的性能,接收器运行特征曲线 (AUROC) 下的面积约为0.86,用于从12导心电图中估计EF.
- 单个心电图线索不足以准确估计EF.
- 特定的ECG组合可以显著提高分类性能.
结论:
- DL模型显示了从12导电心电图估计EF的显著前景,为高频监测提供了可扩展的解决方案.
- 人工智能模型在不同人群中的表现,如阿巴拉契亚农村地区,对于公平的医疗保健至关重要.
- 在心电图分析中优化组合可以提高人工智能驱动的EF估计准确度.
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