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This study presents the results of classification of autofluorescence (AF) and diffuse reflectance (DR) spectra obtained in vivo on skin Basal Cell Carcinomas (BCC) and Squamous Cell Carcinomas (SCC), Actinic Keratoses (AK) and Healthy skin (H) of 140 patients. The bimodal spectroscopic instrument used in this study uses five LEDs for fluorescence excitation at wavelengths peaks between 365 and 415 nm, and a xenon lamp featuring 350-800 nm emission range to obtain AF and DR spectra for four source-detector distances (from 400 to 1000 μm).
The classification (C vs H, H vs AK) was done by support vector machine, discriminant analysis, and multilayer perceptron. Final accuracy of two-class classification tests for almost all pairs of classes was more than 80%. This study presents a comparison of the performance of these combination of methods with the standard clinical procedure.
In the current study, a bimodal spectroscopic device was used to obtain in vivo spatially resolved AF and DR spectra of skin in the visible range. Five LEDs featuring wavelength peaks at 365, 385, 395, 400 and 415 nm and a xenon lamp featuring a 350-800 nm spectral emission were used as light sources. Four source-detector separation (SDS) were used: 400, 600, 800, and 1000 μm.
Spectra were taken in different anatomical sites on 131 patients of different age and gender during a clinical study. Spectra were analysed using classification (support vector machine and multilayer perceptron) and regression (multilayer perceptron, linear, kernel ridge and Lasso) methods. Results of skin phototype and age estimation from AF and DR spectra obtained in vivo using machine learning methods will be presented and discussed.
This study presents the results of classification of cancerous and healthy colon tissue absorption coefficient spectra. The absorption coefficient was measured using direct calculations from the total reflectance and total transmittance spectra obtained ex vivo. Classification was performed using support vector machine, multilayer perceptron and linear discriminant analysis.
Analysis of exhaled air of patients with myocardial infarction by laser spectroscopy and data mining
Additionally to these biomarkers in the MI patients’ blood, there are many other products of metabolism in damaged muscles, which are excreted from the body human body, including through exhaled air. The results of MI patients’ exhaled air analysis using photoacoustic laser spectroscopy and data mining are presented.
The material for this study were the gas emission samples collected from patients and healthy volunteers – samples of exhaled air, swabs from teeth and cheeks. A set of material was formed three groups: healthy volunteers, patients with COPD, lung cancer patients.
The resulting samples were analyzed by means of laser opto-acoustic gas analyzers: with intracavity location detector (ILPA-1), with extracavity location detector (LGA-2). Presentation of the results in an easy to visual form was performed using the method of elastic maps, based on the principal component analysis.
The results of analysis show potentialities of usage of laser optoacoustic spectroscopy application to assess the status of patients with chronic obstructive pulmonary disease and lung cancer.
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