评估脉冲氧计人工智能方法:性能,临床准确性和全面偏差分析
Ana María Cabanas1, Nicolás Sáez1, Patricio O Collao-Caiconte2
1Departamento de Física, FACI, Universidad de Tarapacá, Arica 1000000, Chile.
Bioengineering (Basel, Switzerland)
|November 27, 2024
概括
人工智能 (AI) 增强了使用光聚光学 (PPG) 信号的血氧和 (SpO2) 估计. 高斯过程模型显示出卓越的准确性,但多样化的数据集和可靠的标准对于临床AI应用至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 血氧和 (SpO2) 监测在患者护理中至关重要.
- 传统的 SpO2 估计方法面临着局限性.
- 人工智能 (AI) 提供了通过光聚光学 (PPG) 信号改进SpO2分析的潜力.
研究的目的:
- 从PPG信号中对SpO2估计的AI技术进行系统审查.
- 识别领先的AI模型,功能,数据集和性能指标.
- 评估目前人工智能驱动的SpO2监测中的偏见和识别挑战.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 分析了183个参考文献,其中26项研究符合纳入标准.
- 使用QUADAS-2标准对AI模型 (例如DNNs,高斯过程),特征,数据集和偏差的评估.
主要成果:
- 线性回归和深度神经网络 (DNN) 是常见的AI方法.
- 高斯过程模型表现出高精度 (MAE低至0.57%,RMSE低至0.69%).
- 偏差分析显示,需要更好地选择患者,参考标准和SpO2间隔.
结论:
- 人工智能,特别是高斯过程模型,显示出准确的SpO2估计的前景.
- 临床翻译需要改进参考标准,数据集和伦理考虑.
- 将人工智能与生理模型集成,可以增强SpO2监测和患者护理.
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