Monte Carlo simulation of the effect of melanin concentration on light-tissue interactions for transmittance pulse oximetry measurement.

SIGNIFICANCE

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.

AIM

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.

APPROACH

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.

RESULTS

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.

CONCLUSIONS

This study underpins the careful re-assessment of pulse oximeter designs to minimize bias in measurements across diverse populations.