System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support

The article considers a new approach to solving the problem of increasing the reliability of diagnostics of biological tissues based on a combination of azimuthally invariant polarization and fluorescence methods for diagnosing the structure of optically anisotropic tissue samples. The improved diag...

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Main Authors: N.I. Zabolotna, V.V. Sholota
Format: Article
Language:English
Published: Zhytomyr Polytechnic State University 2025-07-01
Series:Технічна інженерія
Subjects:
Online Access:https://ten.ztu.edu.ua/article/view/334749
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author N.I. Zabolotna
V.V. Sholota
author_facet N.I. Zabolotna
V.V. Sholota
author_sort N.I. Zabolotna
collection DOAJ
description The article considers a new approach to solving the problem of increasing the reliability of diagnostics of biological tissues based on a combination of azimuthally invariant polarization and fluorescence methods for diagnosing the structure of optically anisotropic tissue samples. The improved diagnostic method involves measuring the intensity distributions of autofluorescence polarimetry and azimuthally independent Muller matrix images of optically thin tissue samples at wavelengths of 450 nm, 550 nm, 650 nm, which correspond to the maxima of the emission spectra of the intrinsic fluorophores of biological layers after excitation by a polarized beam. Further statistical processing of the measured distributions, which is carried out in an improved system of laser polarization autofluorescence diagnostics, allows you to form a vector of informative features from estimates of their averages, dispersion, asymmetry and kurtosis, formed at each of the three specified wavelengths. Based on the vector of informative features and the developed fuzzy logic decision support models obtained at three wavelengths, a recommended diagnostic solution is formed. An experimental study of the improved diagnostic method was conducted on a sample of 160 samples of biological myocardial tissues, formed by three groups: (control group – 20 samples; ischemia – 70 samples; acute coronary insufficiency – 70 samples). It was shown that a high (90–92 %) and excellent (95–95,7 % reliability) qualitative level of diagnostic assessment was achieved. An increase in the reliability of myocardial diagnosis from 2,5 to 3,7 % was obtained using the improved method in comparison with analogues.
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publishDate 2025-07-01
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spelling doaj-art-6b14322f674f4d6bbf0d5e29d95e7e012025-08-20T03:09:00ZengZhytomyr Polytechnic State UniversityТехнічна інженерія2706-58472707-96192025-07-01951219227doi.org/10.26642/ten-2025-1(95)-219-227System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision supportN.I. Zabolotna0https://orcid.org/0009-0006-2209-8573V.V. Sholota1https://orcid.org/0000-0002-7073-8727Vinnytsia National Technical University, UkraineVinnytsia National Technical University, UkraineThe article considers a new approach to solving the problem of increasing the reliability of diagnostics of biological tissues based on a combination of azimuthally invariant polarization and fluorescence methods for diagnosing the structure of optically anisotropic tissue samples. The improved diagnostic method involves measuring the intensity distributions of autofluorescence polarimetry and azimuthally independent Muller matrix images of optically thin tissue samples at wavelengths of 450 nm, 550 nm, 650 nm, which correspond to the maxima of the emission spectra of the intrinsic fluorophores of biological layers after excitation by a polarized beam. Further statistical processing of the measured distributions, which is carried out in an improved system of laser polarization autofluorescence diagnostics, allows you to form a vector of informative features from estimates of their averages, dispersion, asymmetry and kurtosis, formed at each of the three specified wavelengths. Based on the vector of informative features and the developed fuzzy logic decision support models obtained at three wavelengths, a recommended diagnostic solution is formed. An experimental study of the improved diagnostic method was conducted on a sample of 160 samples of biological myocardial tissues, formed by three groups: (control group – 20 samples; ischemia – 70 samples; acute coronary insufficiency – 70 samples). It was shown that a high (90–92 %) and excellent (95–95,7 % reliability) qualitative level of diagnostic assessment was achieved. An increase in the reliability of myocardial diagnosis from 2,5 to 3,7 % was obtained using the improved method in comparison with analogues.https://ten.ztu.edu.ua/article/view/334749biological layer diagnostics systempolarizationautofluorescencefuzzy logicdecision support
spellingShingle N.I. Zabolotna
V.V. Sholota
System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
Технічна інженерія
biological layer diagnostics system
polarization
autofluorescence
fuzzy logic
decision support
title System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
title_full System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
title_fullStr System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
title_full_unstemmed System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
title_short System of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
title_sort system of polarization autofluorescence diagnostics of biological layers with fuzzy logic of decision support
topic biological layer diagnostics system
polarization
autofluorescence
fuzzy logic
decision support
url https://ten.ztu.edu.ua/article/view/334749
work_keys_str_mv AT nizabolotna systemofpolarizationautofluorescencediagnosticsofbiologicallayerswithfuzzylogicofdecisionsupport
AT vvsholota systemofpolarizationautofluorescencediagnosticsofbiologicallayerswithfuzzylogicofdecisionsupport