用可穿戴设备对心血管疾病的AI驱动实时监测:范围审查
Ali Abedi1,2, Anshul Verma1,2, Dherya Jain1,2
1KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
JMIR mHealth and uHealth
|November 11, 2025
概括
人工智能和可穿戴设备对实时心血管监测有希望,但研究有限. 进一步验证和应对现实世界的挑战对于广泛的临床采用至关重要.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心血管疾病是全球主要的死亡原因,需要改进检测和预测方法.
- 可穿戴设备提供非侵入性,不断监测心血管健康,但产生大量数据需要先进的AI进行实时分析.
- 有效的临床决策依赖于可穿戴设备对心血管信号的实时分析.
研究的目的:
- 通过可穿戴设备识别人工智能驱动的平台面临的挑战,以实时进行心血管监测.
- 探索潜在的解决方案,并检查人工智能算法开发的强大监控.
- 研究部署管道的优化,以实时进行心血管监测.
主要方法:
- 在六个主要的电子数据库中进行了全面的文献搜索.
- 纳入标准侧重于使用可穿戴设备和人工智能进行实时心血管事件检测或预测的研究.
- 经过严格的选和全文审查,共有19项研究符合纳入标准.
主要成果:
- 使用可穿戴设备对人工智能驱动的实时心血管监测的研究是有限的,缺乏全面的验证.
- 大多数研究都使用了公共数据集,很少有真实世界的基于社区的验证.
- 电心电图可穿戴设备很常见,通常在医院环境中,AI部署在设备上或通过云.
结论:
- 跨学科的研究对于实现人工智能驱动的实时心血管健康管理的潜力至关重要.
- 为持续,基于社区的部署提供可扩展的解决方案需要开发和验证.
- 现实世界的挑战,包括参与者合规性,硬件/连接性和AI模型优化,需要仔细考虑.
关键词:
人工智能的人工智能是人工智能.心房动是心房动的一种.心血管疾病心血管疾病持续的监控,持续的监控.深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.机器学习是机器学习.手机电话 手机电话手机电话实时监控实时监控智能手表的智能手表可穿戴设备可穿戴设备.更多相关视频
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