基于深度学习的动脉血压波形生成,使用光电显微镜进行非侵入性血液动力学监测
Sangha Kim1, Hyeonhoon Lee1, Seong-A Park2
1Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究引入了一种深度学习模型,以非侵入性方式使用光多血图 (PPG) 信号来估计动脉血压 (ABP). 校准模型实现了准确的ABP监测,符合更安全的患者护理的临床标准.
科学领域:
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械
- 人工智能在医学中的应用
背景情况:
- 持续的动脉血压 (ABP) 监测对于血液动力学评估至关重要.
- 目前使用动脉导管的侵入性方法存在风险和局限性.
- 对于广泛的临床应用,需要非侵入性替代品.
研究的目的:
- 开发和验证一个深度学习框架,用于非侵入性ABP波形生成从光聚光谱 (PPG) 信号.
- 提高非侵入性ABP监控的准确性和可靠性.
- 评估拟议方法的临床可行性.
主要方法:
- 一个基于ResUNet的深度学习模型被开发出来,可以从PPG信号中提取ABP波形.
- 每2.5分钟将定期校准值纳入模型.
- 该模型在首尔国立大学医院的4,687个手术内病例的大数据集上进行了训练和验证.
主要成果:
- 校准的深度学习模型实现了平均绝对误差 (MAE) 5.69 mmHg (SD: 3.76 mmHg) 的ABP估计.
- 这显著超过了非校准模型 (MAE:9.47 mmHg,SD:5.52 mmHg).
- 该模型符合AAMI和BHS标准,在静缩血压 (SBP) 中达到B级,在静缩血压 (DBP) 中达到A级.
结论:
- 拟议的深度学习框架能够准确,非侵入性地估计动脉血压.
- 定期校准可以提高从PPG信号生成的ABP波形的真实性.
- 这项技术支持在各种环境中进行临床部署,特别是在侵入性监测不可行的情况下.
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