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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=143332"><dc:title>Development of bruise age determination approach using optical techniques</dc:title><dc:creator>Marin,	Ana	(Avtor)
	</dc:creator><dc:creator>Milanič,	Matija	(Mentor)
	</dc:creator><dc:creator>Majaron,	Boris	(Komentor)
	</dc:creator><dc:subject>pulsed photothermal radiometry</dc:subject><dc:subject>diffuse reflectance spectroscopy</dc:subject><dc:subject>Monte Carlo</dc:subject><dc:subject>bruise characterization</dc:subject><dc:subject>bruise age estimation</dc:subject><dc:description>Estimating the bruise age in the clinic is usually done by visual inspection. In search of a more objective approach to assess the bruise evolution dynamics, various techniques have been tried, with varying degrees of success. The aim of this dissertation is to develop a method to objectively determine age of bruises using optical techniques.
To this end, we have combined two experimental techniques: pulsed photothermal radiometry (PPTR) and diffuse reflectance spectroscopy (DRS). PPTR allows noninvasive monitoring of absorber distribution in a sample, while DRS provides insight into tissue composition through diffusely reflected spectra in the visible and near IR region.
Using a suitable optical skin model and an inverse Monte Carlo model of light propagation in tissue, we can compare the simulated signals with experiments and extract parameters typical of intact and bruised skin. Using a four-layer bruise model, we characterize parameter timelines for blood and its breakdown product bilirubin, blood oxygenation, and skin scattering for severe and less severe incidental bruises as well as for bruises induced in a controlled manner with a small projectile.
To describe the dynamics of bruise evolution in a laterally homogeneous part of the bruise, we used a 1D diffusion equation to calculate hemoglobin distribution, and its degradation, with similar equations for bilirubin generation and removal. Using this dynamical model, we were able to determine typical parameters of bruise dynamics for several bruise sets.
To estimate the bruise age we combined the characteristic timelines of blood and bilirubin content in the skin with the predictions of the dynamical model. We have shown that this hybrid model estimates the bruise age better than by visual impression. The best results are comparable to other age determination techniques that use a mathematical model of bruise evolution.</dc:description><dc:date>2022</dc:date><dc:date>2022-12-15 08:15:02</dc:date><dc:type>Doktorsko delo/naloga</dc:type><dc:identifier>143332</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
