A dental application of neural network computing: Classification of complex electrical impedance measurements to aid root canal treatment

Abstract

Electrical impedance measurements provide an alternative diagnostic technique to the use of radiographs for aiding dental root canal treatment. Analysis of impedance data was based on Complex NonLinear Least Squares (CNLS) regression with electrical circuits as models. Different equivalent circuits were required to model the data at various depths within root canals. Therefore, it was not valid to compare directly the parameter values obtained for the same electrical components when different circuits were used. This problem was solved with a neural computing approach based on supervised training of the backpropagation algorithm to classify the data. Two strategies were investigated. The first produced a network output which indicated the electrode depth within the canal. The second approach employed the neural network as a preprocessor to establish which equivalent circuit was appropriate for the CNLS. Tests were also carried out to determine the minimum number of input nodes required by a neural network for this dental application.