从面部视频中预测血压,使用面部诊断理论和深度神经网络技术
Weiying Xing1, Yinni Shi2, Chaoyong Wu1
1School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China.
Computers in biology and medicine
|July 23, 2023
概括
这项研究引入了一种使用面部视频预测血压的AI框架,用于非侵入性地预测血压. 该系统实现了90%的真实预测率,为高血压查提供了一种方便的方法.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
背景情况:
- 高血压是心血管疾病的主要原因,需要准确和方便的血压监测.
- 使用面部视频进行非接触式血压预测是一个新兴的研究领域.
- 传统中医 (TCM) 使用面部诊断进行健康评估,但其与计算机视觉的整合用于血压预测尚未得到充分探索.
研究的目的:
- 开发和验证人工智能 (AI) 框架,用于使用深 convolutional 神经网络进行非接触式血压预测.
- 评估将基于TCM的面部诊断原则与血压估计的计算机视觉相结合的有效性.
- 调查特定面部区域对预测准确性的影响.
主要方法:
- 从652个面部视频中提取脉冲波信号.
- 9个人工神经网络 (ANN) 的训练和比较,用于血压预测.
- 基于不同面部区域选择的预测模型性能评估.
主要成果:
- 开发了一个利用深层卷积神经网络的人工智能框架.
- 性能最好的预测模型实现了90%的整体真预测率.
- 选择五个特定的面部区域显著改善了血压预测的准确性.
结论:
- 拟议的AI框架证明了计算机辅助血压预测的高效性和稳定性.
- 这种方法为高血压查和心血管疾病预防提供了客观和方便的方法.
- 面部视频分析作为健康监测的非侵入性工具具有前景.
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