使用多点心电图和深度学习识别心力衰竭动态
Yu Nishihara1, Makoto Nishimori2, Satoki Shibata3
1Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Hospital, 7-5-2, Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan.
European heart journal. Digital health
|May 21, 2025
概括
一个新的深度学习模型使用心电图 (ECG) 准确检测心力衰竭 (HF) 状态变化. 这种非侵入性工具有助于早期干预和持续的高频监测,改善患者的治疗结果.
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:
- 心力衰竭 (HF) 住院病例与生存率低下有关,需要早期干预.
- 深度学习 (DL) 显示出通过心电图 (ECG) 检测高频率的前景,但其在持续监测中的作用尚不清楚.
- 目前的监测方法可能是侵入性的或昂贵的,突出了需要可访问的替代品.
研究的目的:
- 开发和评估一个DL模型,用串行ECG检测高频状态的变化.
- 加强早期干预和持续高频监测能力,在不同的医疗保健机构.
- 评估模型在分类高频状态变化 (恶化,改善,无变化) 中的性能.
主要方法:
- 基于变压器的DL模型使用来自6,531名成年患者的30171个心电图进行了开发.
- 在两个不同的时间点收集了ECG,以分析HF状态的变化.
- 模型性能使用AUROC和准确度进行评估;注意力映射用于可解释性.
主要成果:
- 对于高频状态分类,DL模型实现了0.889的AUROC和0.871的准确性.
- 该模型有效地根据ECG波形信号识别了高频状态的变化.
- 注意力映射为模型的决策过程提供了洞察力.
结论:
- 基于变压器的DL模型准确地检测高频状态变化,提供一种非侵入性的监控工具.
- 这种方法减少了对侵入性和昂贵的诊断程序的需求.
- 该模型具有可访问和高效的高频管理的潜力,支持早期干预.
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