Abstract
Fluorescence spectroscopy presents great interest for the diagnosis of atherosclerosis. Nevertheless there are some difficulties in the interpretation of diagnostic information. This could be overcome by precise methods of extraction of the diagnostic parameters and convenient statistical analysis, which are the subject of this work.
Fluorescence excitation-emission matrices from different categories of coronary arteries were developed and used to derive the optimum excitation wavelength on the one hand and to assign the spectra to specific chromophores on the other hand. Simple dimensionless functions (F) were formed by the ratio of the intensities at selected wavelength and the logistic model was used for statistical analysis. Decision surfaces were drawn and it was estimated that the probability of correct classification is 88%. The algorithm correctly diagnoses 97% of healthy from diseased samples and 80% of fibrous from calcified coronary arteries.
Get full access to this article
View all access options for this article.