一种物理集成的深度学习方法,用于使用PPG进行患者特定的非牛顿血粘度评估
Hyeong Jun Lee1, Young Woo Kim1, Seung Yong Shin2
1Division of Biomarkers, Imaging, and Hemodynamic Studies (BIOS), Department of Mechanical Engineering, Yonsei University, Seoul, Korea; Center for Precision Medicine Platform Based on Smart Hemo-Dynamic Index (SHDI), Seoul, Korea.
Computer methods and programs in biomedicine
|March 30, 2025
概括
这项研究使用新型人工智能模型从光聚光显微镜 (PPG) 中提取患者特异性的血液粘度. 这项创新扩大了对循环系统疾病的可穿戴健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 生理测量生理测量
- 医疗保健中的人工智能
背景情况:
- 越来越多的老年人群需要先进的远程,非侵入性健康监测.
- 目前的可穿戴设备不能有效地监测循环系统.
- 非侵入性地测量血液粘度可以显著提高可穿戴健康技术.
研究的目的:
- 开发一种方法,从光电显微镜 (PPG) 数据中提取患者特定的血液粘度方程.
- 通过可穿戴设备来监测循环系统的健康状况.
- 扩大非侵入性健康监测系统的应用范围.
主要方法:
- 开发了一种混合的1D卷积神经网络-长期短期记忆 (CNN-LSTM) 架构.
- 纳入了基于物理学的约束,整合了学的原则.
- 该模型使用k-fold交叉验证进行训练,优化基于卡罗-亚苏达模型的损失函数的重量.
主要成果:
- 估计模型的整体准确度为81.1%,在生理剪切范围 (50-300s-1) 中准确度为84.0%.
- 在生理学范围中,平均绝对误差为0.67cP,处于临床粘度计容忍度 (<1cP) 之内.
- 在预测和基本真相值之间观察到强烈的线性关系 (相关系数:0.619-0.742,p < 0.0001).
结论:
- 来自PPG的血液粘度的非侵入性估计可以显著提高可穿戴医疗保健系统的诊断能力.
- 这种方法显示了针对各种循环系统疾病的潜力.
- 在生理学上相关的剪切范围中证明的准确性支持潜在的临床应用.
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