从美国退伍军人那里,深度学习心电图的鼻节奏,以预测心房动
Neal Yuan1,2, Grant Duffy3,4, Sanket S Dhruva1,2
1Department of Medicine, University of California, San Francisco.
JAMA cardiology
|October 18, 2023
概括
深度学习模型可以使用在正常鼻节律期间采用的心电图 (ECG) 预测心房动 (AF). 这种早期检测可能有助于预防中风和其他心血管事件.
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:
- 早期发现心房动 (AF) 对于预防诸如中风等不良心血管事件至关重要.
- 深度学习 (DL) 在通过心电图 (ECG) 来识别各种心血管疾病方面取得了成功.
研究的目的:
- 评估DL模型在预测AF中的有效性从外科医院ECGs在一个大,多样化的患者群体内鼻节律.
- 评估DL在常规临床实践中用于早期AF识别的潜力.
主要方法:
- 在美国退伍军人事务 (VA) 医院网络的12导电心电图上训练了一个卷积神经网络,以便在31天内预测AF.
- 该模型在来自多个VA网络的ECG和一个大型非VA学术医疗中心的ECG上得到了验证,包括不同的人口群体.
- 预测研究利用1987年至2022年的门诊心电图的大数据集.
主要成果:
- DL模型在VA站点实现了0.86的AUROC,在非VA站点达到0.93,在31天内显示出强大的AF预测性能.
- 精度和F1评分表明,在各种患者群体中表现强,包括黑人患者,女性和高CHA2DS2-VASc评分的患者.
- 该模型显示了良好的校准 (布赖尔分数为0.02) 和高效的查潜力,需要查高风险个体的数量很少.
结论:
- 鼻节律ECG的DL分析可以有效地预测未来的AF诊断,跨越各种患者人口统计和并发症.
- 这些发现支持将基于DL的ECG分析整合到查计划中,以减轻AF相关的并发症.
- 该研究强调了人工智能在彻底改变心血管疾病检测和管理方面的潜力.
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