Prediction of thrust force and torque in canal preparation process using Taguchi method and Artificial Neural Network

Abstract

Root canal preparation is a vital procedure during the treatment of pulposis and periapical periodontitis. However, the improper control of thrust force and torque in root canal preparation will cause nerve damage and cell necrosis. The aim of this study was to investigate and optimize the main factors influencing thrust force and torque and to establish an efficient predictive model for root canal preparation. This study was conducted on fresh bovine bones due to the similarity of structure and density with human teeth. A novel experimental platform was first built to measure the force and torque in canal preparation of different parameters. The effect of the experimental results on thrust force and torque was investigated based on Analysis of variance (ANOVA). The results indicated that the diameter of instrument, width of root canal, and feed rate are the most significant factors influencing the thrust forces and torque ( p < 0.05). Based on the above experiments, a Radial Basis Function Neural Network (RBFN) model was established to predict the thrust force and torque in a wider range of parameters. In confirmation tests, RBFN showed an excellent predictive model for prediction of thrust force and torque (error less than 14%) in canal preparation.