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.

Methods

Once reflection data has been simulated in MCMatlab, we can use iSetCam to model imaging skin with the specified reflectance data. To start we first combine reflection data for various skin types into a single scene; this is useful for easy comparison between skin properties.

With the scene created from the reflectance data, we can use iSetCam to adjust the illuminant on the scene. Using illuminants with different spectral densities, different portions of the spectrum can be probed more or less heavily. The simulated reflectance of skin may show more or less sensitivity to blood oxygen levels and skin melanin content around specific wavelengths. Therefore tuning the spectral density to probe these regions of interest explores how illuminant conditions affect the distinguishability of skin characteristics. In our study, we include both broadband white light sources and LED-like sources with high spectral density around a single wavelength.

The scene and illuminant combination represents the light that would be expected to be reflected back from the scene given the illuminant’s spectral density. This light can then be processed at a sensor to model how different sensors respond.

Using iSetCam the sensor properties can be adjusted; many characteristics can be explored including, pixel size, noise properties, filter arrays and many more. In this study, we are largely interested in the spectral response of the light, and how to tune the illuminant spectrum and the sensor filter spectrum to best capture various skin features. In this case we kept noise properties to include both electronic and shot noise in all cases, as well as imaging conditions like field-of-view and imaging distance constant. We considered real sensors based on data from physical and purchasable items, as well as idealized sensors. For our study we investigated both monochrome or intensity sensors, as well as color sensors.

Once a filter with specific properties has been set, the light can be processed at that sensor. This leads to expected pixel values that can be considered in either voltages or digital values. To predict distinguishability we pull these pixel values and look at the average value for each set of skin characteristics. We can evaluate these pixels by looking at which filters provide the most and least sensitivity to skin characteristics to lay the groundwork for data processing algorithms to accurately describe skin characteristics. Considering low sensitivity sensor responses is useful to calibrate the data, and then use high sensitivity portions to accurately read the value after calibration.

Results

Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin.