Skin Tone Calibration of Pulse Oximeter Oxygen Saturation Data

Our project aims to enhance the accuracy of pulse oximetry readings across all skin tones to address the critical issue of hidden hypoxemia, particularly prevalent among individuals with darker skin. Our innovative approach uses machine learning to estimate oxygen saturation estimation (SaO2) from standard SpO2 readings by integrating additional patient health data and a race-dependent quantification of skin tone. We hope to detect hypoxemia more accurately, reduce racial disparities in healthcare, and improve overall patient outcomes. This initiative is backed by studies showing that existing SpO2 readings often fail to reveal low oxygen levels in darker-skinned patients, leading to potentially severe undiagnosed conditions. Our approach is to ensure that pulse oximetry—a vital tool in medical diagnostics—is reliable and equitable for all patients.