利用手术室的信号来估计使用AnesthNet的平均动脉压
Jade Perdereau1,2,3, Jona Joachim4,5,6, Fabrice Vallée4,5,6
1INSERM, U942 MASCOT, Université Paris Cité, Paris, 75006, France. jade.perdereau@inria.fr.
Scientific reports
|September 30, 2025
概括
新型深度学习模型AnesthNet使用常规传感器进行非侵入性估计平均动脉压 (MAP). 这种人工智能方法在麻醉监测中提供了比动脉线更安全的替代方案.
科学领域:
- 麻醉学 麻醉学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 监测平均动脉压 (MAP) 在全身麻醉期间至关重要.
- 目前的方法,袖口测量和侵入性动脉线路,具有显著的局限性.
- 迫切需要准确的,非侵入性的MAP监测替代方案.
研究的目的:
- 开发和评估AnesthNet,这是一个用于非侵入性MAP估计的深度学习架构.
- 解决现有的非侵入性MAP监测技术的局限性,包括可重现性和实时预测.
- 将AnesthNet的性能与最先进的深度学习模型进行比较.
主要方法:
- 麻醉网的设计是使用来自非侵入性传感器的数据:光电心电图,心电图和袖口振荡计.
- 该模型在两个大型数据集VitalDB (2,833名患者) 和LaribDB (5,060名患者) 上进行了评估.
- 用国际标准对领先的深度学习架构进行了性能评估,考虑了不同的袖口值延迟和实时推断.
主要成果:
- 在VitalDB上,AnesthNet表现出卓越的性能,平均绝对误差 (MAE) 为4.6 (± 4.7) mmHg,在LaribDB上为3.8 (± 5.7) mmHg.
- 该模型在不同的袖口值延迟方面表现优于其他架构.
- 麻醉网实现了实时推断,没有显著的延迟.
结论:
- 麻醉网为麻醉期间的MAP监测提供了一种临床上可行的非侵入性方法.
- 与当前方法相比,深度学习方法提供了更好的准确性和安全性.
- 这种人工智能驱动的解决方案满足麻醉监测实时临床要求.
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