基于心电图的机器学习算法的开发和验证,用于在人口层面进行心血管诊断
Sunil Vasu Kalmady1,2,3, Amir Salimi1, Weijie Sun1
1Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
NPJ digital medicine
|May 18, 2024
概括
人工智能 (AI) 算法现在可以从心电图 (ECG) 预测15种心血管疾病. 深度学习模型表现出强的性能,优于早期心血管疾病检测的传统方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 电心电图 (ECG) 对于心血管 (CV) 诊断至关重要.
- 人工智能正在增强超越传统措施的条件的ECG解释.
- 早期发现各种心血管疾病仍然是一个临床挑战.
研究的目的:
- 开发和验证人工智能模型,使用心电图同时预测15种常见的心脏病诊断.
- 评估深度学习 (DL) 和极端梯度增强 (XGB) 模型的性能.
- 评估AI在人口水平CV条件识别方面的能力.
主要方法:
- 对来自244077名成年患者的1,605,268个心电图进行了回顾性研究 (加拿大阿尔伯塔省,2007-2020年).
- 利用基于ResNet的深度学习 (DL) 在ECG跟踪和XGB在ECG测量上.
- 包括15个国际疾病分类,第10次修订 (ICD-10) CV诊断.
主要成果:
- 对于4种诊断 (AVB,HCM,MS,STEMI),DL模型实现了接收器操作特征曲线 (AUROC) 下面的面积>90%.
- 在8个条件 (CA,NSTEMI,VT,MVP,PHTN,AS,AF,HF) 中,AUROC在80-90%之间.
- 在AUROC中,DL模型的表现大约比XGB模型的表现要好5%左右.
结论:
- 用人工智能驱动的心电图分析表明,对于常见的心血管疾病的预测性能良好至优异.
- DL模型显示了早期和同时检测多个心血管诊断的显著潜力.
- 这些发现支持将人工智能纳入常规心电图解释中,以改善心血管护理.
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