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Ufuk Bal1, Faruk Enes Oguz2,3, Kubilay Muhammed Sunnetci1

  • 1Department of Electrical and Electronics Engineering, Osmaniye Korkut Ata University, 80000 Osmaniye, Türkiye.

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这项研究介绍了使用面部视频和3D CNN估计血红蛋白水平的非接触方法. 这种创新为诊断各种疾病提供了比传统血液检测更快,更便捷的替代方案.

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三维剩余CNN深度回归面部视频分析医学成像AI无接触式 SpHb非侵入性监测血红蛋白总量

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科学领域:

  • 生物医学工程
  • 医疗诊断
  • 计算机视觉

背景情况:

  • 血红蛋白测量对于诊断感染,创伤和贫血至关重要.
  • 目前的方法 (血液采样,脉冲氧计) 有时间,成本和身体接触等限制.
  • 在远程或紧急情况下需要非侵入性,无接触的方法.

研究的目的:

  • 开发和验证一种非接触式的自动化方法来估计总血红蛋白水平.
  • 使用面部视频数据和3D卷积回归模型进行血红蛋白估计.
  • 为传统的血红蛋白测量技术提供实用替代方案.

主要方法:

  • 收集了279名志愿者的数据集,并同步了面部视频和脉冲氧计血红蛋白数据.
  • 训练了三维 (3D) 卷积神经网络 (CNN) 回归模型 (3D CNN,注意力增强的3D CNN,剩余的3D CNN).
  • 模型的性能使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和皮尔森相关系数进行了评估.

主要成果:

  • 剩余的3D CNN模型在测试中表现最好.
  • 达到了1.06的RMSE,0.85的MAE和0.73的皮尔森相关系数.
  • 开发的模型在一个用户友好的图形界面中实现.

结论:

  • 从面部视频进行无接触血红蛋白估计是可行的,使用3D CNN回归.
  • 这种方法为血红蛋白水平的评估提供了一个有希望的,非侵入性的工具.
  • 潜在的应用包括远程患者监测和快速诊断查.