从手到代码:机器学习在非侵入性血压监测中
Ravi Pal1, Joshua Le2, Theodora Wingert1
1Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, California, United States of America.
Anaesthesia, critical care & pain medicine
|November 6, 2025
概括
使用AI和ML的连续无袖血压 (BP) 监测正在出现. 这些来自ECG和PPG等信号的技术承诺可穿戴设备,用于动态的患者护理,超越传统的重袖子.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管监测 心血管监测
背景情况:
- 精确的血压 (BP) 测量对于管理高血压和低血压至关重要,这些常见的疾病往往没有症状,直到器官损伤发生.
- 传统的基于袖子的血压装置是重的,不方便的,只提供间歇性读数,限制了连续监测.
- 为了积极的医疗保健和以患者为中心的管理,人们越来越需要方便的,持续的血压监测.
研究的目的:
- 为提供非侵入性BP测量技术的全面概述.
- 强调新兴的AI和ML驱动的方法用于BP估计.
- 讨论连续和可穿戴BP监控解决方案的趋势.
主要方法:
- 关于非侵入性血压测量技术的现有文献的审查.
- 专注于AI/ML方法,利用心电图 (ECG) 和光电显微镜 (PPG) 等生理信号.
- 分析技术的发展,以实现连续和无袖的BP监测.
主要成果:
- 对AI/ML显著感兴趣,用于从非侵入性信号中估计血压.
- 通过可穿戴设备进行连续,无袖的血压监测的潜力.
- 进步使更动态和以患者为中心的血压评估成为可能.
结论:
- 人工智能和机器学习驱动的技术代表了非侵入性BP监测的重大演变.
- 新兴的解决方案承诺持续的,无袖式的血压评估,提高患者的护理.
- 专注于概念和技术的发展,临床验证正在进行中.
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