Mitigating Skin Pigmentation Bias in Pulse Oximetry through Personalized Machine Learning Models

Carter D. Ostrowski, Hakan Burak Karli, Bige Deniz Unluturk

Confirmation: I have read and agree with the IEEE BHI 2025 conference submission's policy on behalf of myself and my co-authors.

Keywords: pulse oximeter bias, machine learning, personalization

TL;DR: This study presents a personalized ML approach that improves pulse oximetry accuracy by incorporating skin pigmentation and physiological data, reducing bias and enhancing equitable monitoring across diverse populations.

Abstract: Pulse oximeters are essential in neonatal care for monitoring blood oxygen saturation, however their accuracy can be affected by skin pigmentation. The discrepancy between arterial oxygen saturation (SaO2) and saturation measured by pulse oximeters (SpO2) is more pronounced for darker skin tones, increasing the risk of occult hypoxemia. This study introduces a personalized machine learning approach aimed at reducing measurement bias by integrating objective, non-invasive skin pigmentation metrics alongside individual physiological parameters. Using the OpenOximetry Repository, several feature sets were constructed to compare the performance of various machine learning models. XGBoost achieved the lowest root mean square error and was selected for further analysis. The model demonstrated improved SpO2 accuracy, resulting in corrected values which are more closely aligned with actual SaO2 values across a range of skin pigmentation levels. These results support the potential of personalized models to improve measurement accuracy and reduce disparities in clinical monitoring.

Track: 1. Biomedical Sensor Informatics

Registration Id: 5VNK3XYM9V9

Submission Number: 392