Skin microcirculatory responses: A potential marker for early diabetic neuropathy assessment using a low-cost portable diffuse optical spectrometry device.

Diffuse optical measurement is an evolving optical modality providing a fast and portable solution for microcirculation assessment. Diffuse optics in static and dynamic modalities are combined here in a system to assess hemodynamics in skin tissues of control and diabetic subjects. The in-house developed system consists of a laser source, fiber optic probe, a low-cost avalanche photodiode, a finite element model (FEM) derived static optical property estimator, and a software correlator for continuous flow monitoring through microvasculature. The studies demonstrated that the system quantifies the changes in blood flow rate in the immediate skin subsurface. The system is calibrated with in vitro flow models and a proof-of-concept was demonstrated on a limited number of subjects in a clinical environment. The flow changes in response to vasoconstrictive and vasodilative stimuli were analyzed and used to classify different stages of diabetes, including diabetic neuropathy.

Quantification of soft tissue parameters from spatially resolved diffuse reflectance finite element models.

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique that helps in clinical diagnosis of various tissue microcirculation and skin pigmentation disorders based on collected backscattered light from multi-layered tissue. The extraction of the optical properties from the reflectance spectrum using analytical solutions is laborious. Model-based light tissue interaction studies help in quantifying the optical properties. This work presents the use of finite element models of light tissue interaction for this purpose. A bilayer model mimicking human skin was considered and the diffused reflectance spectra at multiple detector points were generated using finite element modelling for varying melanin concentration, epidermal thickness, blood volume fraction, oxygen saturation and scattering components. The reflectance value based on varying optical parameters from multiple detection points lead to the generation of a look-up table (LUT), which is further used for finding the tissue parameters that contribute to the spatially resolved reflectance values. The tissue parameters estimated after inverse modelling showed a high degree of agreement with the expected tissue parameters for a test dataset different from the training dataset.