深度学习用于心脏过载估计 - 从心脏声音和心电图中预测B型尿素 (BNP) 水平
Shimpei Ogawa1, Masanobu Ishii2,3, Shumpei Saito1
1AMI Inc.
概括
一种新的深度学习模型使用心脏声音和心电图估计B型性尿素 (BNP) 水平,为心力衰竭 (HF) 查提供了一种非侵入性工具. 这种方法对快速,客观的护理点HF检测有希望.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- B型性 (BNP) 和N终端亲BNP (NT-pro-BNP) 是心力衰竭 (HF) 管理的关键生物标志物.
- 对于HF的传统听觉缺乏客观的评估.
- 声心电图仪器可以快速记录心脏声音和心电图.
研究的目的:
- 开发一种深度学习模型,通过非侵入性心脏声音和心电图信号来估计血BNP水平.
- 用外部数据集验证模型的性能.
- 评估这种非侵入性方法对HF查的临床可行性.
主要方法:
- 开发了一种深度学习模型,使用8秒钟的心声和心电图记录来估计BNP水平.
- 该模型在140名患者的外部验证数据集上进行了评估.
- 对具有特定体重指数范围的患者进行了亚组分析.
主要成果:
- 估计的BNP (eBNP) 模型实现了0.895的接收器运行特征曲线 (AUROC) 下的面积,用于预测BNP水平≥100 pg/mL.
- 敏感度和特异性分别为84.3%和82.9%.
- 在一个子组 (BMI 18.5-25),该模型显示了增强的预测能力,AUROC为0.959,灵敏度为92.5%,特异性为84.8%.
结论:
- 该eBNP模型显示了非侵入性和快速心力衰竭查的巨大潜力.
- 它的客观和简单的性质使它适合于点的护理测试.
- 该模型在不同人群中的稳定性,在独立数据集上得到验证,支持其用于早期HF诊断和监测.
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