Dental Tissue Absorption Spectroscopy

Oral health problems are closely associated with the analysis of dental tissue changes and the stomatologic treatment that follows. The associated paper explores the use of diffuse reflectance spectroscopy in the detection of dental tissue disorders. The data set includes 78 out of 343 measurements of teeth spectra in the wavelength range from 400 to 1700 nm. The proposed methodology focuses on computational and statistical methods and the use of these methods for the classification of dental tissue into two classes (healthy and unhealthy) by estimating the probability of class membership. Signal processing is based on the difference between the healthy and unhealthy teeth reflectance spectra in the infrared and visible ranges. Selected features associated with observed spectra are then used for machine learning classification based on the experience of an expert in stomatology during the learning stage. The dataset includes the graphical user interface for the use of absorption spectroscopy data in the evaluation of tissue quality changes.