Digital Twin for Imaging Skin

Introduction

Blood oxygen saturation is an important health indicator. When blood oxygen levels drop below a certain threshold, extremely dangerous, even life-threatening, conditions can occur due to the lack of oxygen reaching vital organs. Platforms to monitor blood oxygen saturation have therefore been a sought-after solution, as seen in the development of pulse oximeter finger monitors and the addition of monitors onto smartwatches, such as the Apple Watch. Both of these types of monitors use reflected light to perform measurements since oxygen levels affect the reflectance characteristics of blood within the skin.

Another skin chromophore that affects reflectance characteristics of skin is melanin content. The distribution of melanin throughout the skin is an important indicator for skin cancers and skin health, and can affect blood oxygen measurements, making it imperative to consider.

For our project, we explore how imaging sensors can capture differences in blood oxygen levels and skin melanin content. Using imaging sensors for this task is attractive to naturally provide spatial information on the levels of the two chromophores of interest. In our project, we first explore methods to model the skin using MCMatlab [1], and simulate changes in the reflectance spectra given changes in blood oxygen levels and/or skin melanin content. Then we model imaging skin with these characteristics using iSetCam [2], to predict how differences will be captured.