City, University of London, Research Centre for Biomedical Engineering, London, United Kingdom.
J Biomed Opt. 2024 Jun;29(Suppl 3):S33305. doi: 10.1117/1.JBO.29.S3.S33305. Epub 2024 Aug 13.
Questions about the accuracy of pulse oximeters in measuring arterial oxygen saturation ( ) in individuals with darker skin pigmentation have resurfaced since the COVID-19 pandemic. This requires investigation to improve patient safety, clinical decision making, and research.
We aim to use computational modeling to identify the potential causes of inaccuracy in measurement in individuals with dark skin and suggest practical solutions to minimize bias.
An model of the human finger was developed to explore how changing melanin concentration and arterial oxygen saturation ( ) affect pulse oximeter calibration algorithms using the Monte Carlo (MC) technique. The model generates calibration curves for Fitzpatrick skin types I, IV, and VI and an range between 70% and 100% in transmittance mode. was derived by inputting the computed ratio of ratios for light and dark skin into a widely used calibration algorithm equation to calculate bias ( ). These were validated against an experimental study to suggest the validity of the Monte Carlo model. Further work included applying different multiplication factors to adjust the moderate and dark skin calibration curves relative to light skin.
Moderate and dark skin calibration curve equations were different from light skin, suggesting that a single algorithm may not be suitable for all skin types due to the varying behavior of light in different epidermal melanin concentrations, especially at 660 nm. The ratio between the mean bias in White and Black subjects in the cohort study was 6.6 and 5.47 for light and dark skin, respectively, from the Monte Carlo model. A linear multiplication factor of 1.23 and exponential factor of 1.8 were applied to moderate and dark skin calibration curves, resulting in similar alignment.
This study underpins the careful re-assessment of pulse oximeter designs to minimize bias in measurements across diverse populations.
自 COVID-19 大流行以来，人们再次对脉搏血氧仪在测量深色皮肤个体动脉血氧饱和度（ ）的准确性提出质疑。这需要进行调查，以提高患者安全性、临床决策和研究水平。
我们旨在使用计算模型来确定在深色皮肤个体中测量不准确的潜在原因，并提出切实可行的解决方案来最大程度地减少偏差。
我们开发了一种人体手指模型，以使用蒙特卡罗（MC）技术探索黑色素浓度和动脉血氧饱和度（ ）变化如何影响脉搏血氧仪校准算法。该模型在透射模式下为 Fitzpatrick 皮肤类型 I、IV 和 VI 生成校准曲线，并在 70%至 100%的 范围内生成 。通过将计算出的明暗皮肤比值输入到广泛使用的校准算法方程中，计算出偏差（ ），从而得出 。我们将这些结果与实验研究进行了验证，以证明蒙特卡罗模型的有效性。进一步的工作包括应用不同的乘法因子来调整中度和深色皮肤校准曲线相对于浅色皮肤的曲线。
中度和深色皮肤校准曲线方程与浅色皮肤不同，这表明由于不同表皮黑色素浓度下光的不同行为，特别是在 660nm 处，单一算法可能不适合所有皮肤类型。在队列研究中，从蒙特卡罗模型来看，白人受试者和黑人受试者的平均偏差比值分别为 6.6 和 5.47。对于中度和深色皮肤校准曲线，应用线性乘法因子 1.23 和指数因子 1.8，结果曲线相似。
这项研究支持对脉搏血氧仪设计进行仔细重新评估，以最大程度地减少在不同人群中测量 时的偏差。
