Fluorescence spectrometry based chromaticity mapping, characterization, and quantitative assessment of dental caries.

PURPOSE: Dental caries detection, especially the accurate detection of early caries, facilitates prompt interventions. It is reasonably common to use fluorescence imaging for classification and evaluation of caries, but lacks a quantitative, precise and easy-to-use characterization for practical applications. In this study a quantitative approach for caries stage detection by correlating caries spectral and chromatic features was examined. METHODS: A 405 nm LED light source was used as the excitation source. A hyperspectral imaging camera is employed to collect 336 spectral data of different caries stages. Four critical intervals for different stages of caries were extracted by fluorescence spectral features. The mapping relationship between caries spectral and chromatic features was established by Fast Formula Fitting (FFF) and Neural Network Fitting (NNF) methods. RESULTS: The 470-780 nm spectral power distribution was proved to be the best matching color waveband guiding the selection of filters in future instrument development. The correlation coefficients for the two fitting methods were 0.990 and 0.999, respectively. Both methods achieved caries stage prediction at the pixel level with high accuracy using color information. The visualization region in the chromaticity diagram was created. CONCLUSIONS: This quantitative method enables accurate prediction of caries on the entire tooth surface and facilitates the development of portable and low-cost caries detection instruments.