支持人工智能的新型心房动诊断系统使用3D脉冲感知灵活的压力传感器阵列
Yujie Cao1, Ping Li2, Yirun Zhu1
1Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electric Engineering, Soochow University, Suzhou 215137, China.
ACS sensors
|January 6, 2025
概括
使用传统中医原理,可穿戴传感器和人工智能的新系统可以检测心房动 (AF). 这种创新方法在识别AF方面实现了高精度,有助于诊断和远程治疗.
科学领域:
- 心血管医学 心血管医学
- 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:
- 心房动 (AF) 是一种常见的心血管疾病,具有显著的残疾和死亡率.
- 及时有效地识别AF对于诊断和预防至关重要.
- 目前的诊断方法可能缺乏可访问性或实时功能.
研究的目的:
- 为AF开发一种新的智能传感和识别系统.
- 整合传统中医 (TCM) 理论与灵活的可穿戴电子设备和人工智能 (AI).
- 建立一个新的策略,用于精确诊断和远程治疗AF.
主要方法:
- 根据TCM理论设计了灵活的压力传感器阵列,以捕获3D脉冲数据 (Cun,Guan,Chi).
- 开发了一个信号采集系统来记录脉冲信号,由心血管疾病医生标记.
- 利用卷积神经网络 (CNN) 和脉冲数据库创建一个AF识别模型.
主要成果:
- 该系统成功捕获了同步的3D脉冲数据,区分了具有和没有AF的个体.
- 基于人工智能的识别模型在实验室环境中实现了高达90%的识别率.
- 在医院的临床验证表明,检测AF患者的识别率为80%.
结论:
- 拟议的系统为AF的精确诊断和远程治疗提供了一种新的策略.
- 这种TCM,可穿戴电子和AI的整合加速了现代中医治疗的发展.
- 智能传感系统对改善AF管理和患者的治疗结果充满希望.
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