Open AccessTissue intrinsic fluorescence recovering by an empirical approach based on the PSO algorithm and its application in type 2 diabetes screening
Author(s) -
Yuanzhi Zhang,
Huayi Hou,
Zhongzhen Yang,
Yikun Wang,
Ling Zhu,
Meili Dong,
Yong Liu
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.001795
Subject(s) - autofluorescence , particle swarm optimization , support vector machine , fluorescence , sensitivity (control systems) , diffuse reflectance infrared fourier transform , materials science , optics , computer science , noise (video) , fluorescence spectroscopy , monte carlo method , biological system , artificial intelligence , algorithm , chemistry , physics , mathematics , image (mathematics) , statistics , biology , engineering , catalysis , biochemistry , photocatalysis , electronic engineering
In order to reduce the influence of scattering and absorption on tissue fluorescence spectra, after tissue fluorescence and diffuse reflectance in different tissue optical properties were simulated by the Monte Carlo method, a tissue intrinsic fluorescence recovering algorithm making use of diffuse reflectance spectrum was developed. The empirical parameters in the tissue intrinsic fluorescence recovering algorithm were coded as a particle in the solution domain, the classification performance was defined as the fitness, and then a particle swarm optimization (PSO) algorithm was established for empirical parameters optimization. The skin autofluorescence and diffuse reflectance spectra of 327 subjects were collected in Anhui Provincial Hospital. The skin intrinsic autofluorescence spectra were recovered by using the empirical approach and the integration area of the spectra were calculated as fluorescence intensity. Receiver operating characteristic (ROC) analysis for fluorescence intensity was applied to evaluate the classification performance in type 2 diabetes screening. In addition, a support vector machine (SVM) method was implemented to improve the performance of the classification. The results showed that the sensitivity and specificity were 32% and 76% respectively, and the area under the curve was 0.54 before recovering, while the sensitivity and specificity were 72% and 86% respectively, and the area under the curve was 0.86 after recovering. Furthermore, the sensitivity and specificity increased to 83% and 86% respectively when using linear SVM while 84% and 88%, respectively, when using nonlinear SVM. The results indicate that using the tissue fluorescence spectrum recovery algorithm based on PSO can improve the application of tissue fluorescence spectroscopy effectively.