Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic.

Feasibility of fiber optic displacement sensor scanning system for imaging of dental cavity.

The purpose of this study is to investigate the potential of intensity modulated fiber optic displacement sensor scanning system for the imaging of dental cavity. Here, we discuss our preliminary results in the imaging of cavities on various teeth surfaces, as well as measurement of the diameter of the cavities which are represented by drilled holes on the teeth surfaces. Based on the analysis of displacement measurement, the sensitivities and linear range for the molar, canine, hybrid composite resin, and acrylic surfaces are obtained at 0.