Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement

Xiaoyue Qiao,Xin Chen,Zheng Zhang

doi:10.3390/app10217430

Abstract

Root canal therapy is the most fundamental and effective approach for treating endodontics and periapicalitis. The length of the root canal must be accurately measured to clean the pathogenic substances in it. This study aims to present a multifrequency impedance method based on a neural network for root canal length measurement. A circuit system was designed which generates a current of frequencies from 100 Hz to 20 kHz in order to augment the data of impedance ratios with different combinations of frequencies. Several impedance ratios and other quantified characteristics, such as the type of tooth and file, were selected as features to train a neural network model that could predict the distance between the file and apical foramen. The model uses leave-one-out cross-validation, adopts the Adam optimizer and regularization, and has two hidden layers with nine and five nodes, respectively. The neural network-based multifrequency impedance method exhibits nearly 95% accuracy, compared with the dual-frequency impedance ratio method (which demonstrated no more than 85% accuracy in some situations). This method may eliminate the influence of human and environmental factors on measurement of the root canal length, thereby increasing measurement robustness.

Highlights

At present, root canal therapy is the most effective treatment for pulpal and periapical diseases.The keys to the process include thoroughly cleaning the root canal system, removing the source of infection, and reducing the damage to the root tip and periapical tissue [1,2]
Given the inadequacy of the current methodologies, this paper proposes a method for precisely locating the position of the apical foramen using a multifrequency impedance method based on a neural network, which is trained by distinct combinations of multifrequency signals generated from the designed circuit system
Before the training of the neural network for root canal length measurement, the experimental results were discussed through the relationships between impedance values and different variables in order to determine that the theories about the impedance characteristics of teeth are correct, which can verify the feasibility of the method in this study

Summary

Introduction

Root canal therapy is the most effective treatment for pulpal and periapical diseases.The keys to the process include thoroughly cleaning the root canal system, removing the source of infection, and reducing the damage to the root tip and periapical tissue [1,2]. Root canal measurement was initially based on a resistance model, according to the phenomenon that the direct current resistance between the apical foramen and oral mucosa is almost constant, the ages, tooth types, and root canal shapes of patients differ widely. The performance of this model has generally been considered unsatisfactory, as the measurement does not reflect a simple resistance model but, rather, a complex model with capacitor characteristics [6,7]. Root canal measurement was carried out by a voltage gradient method, which was more accurate than before but still unstable [8,9]

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Conclusion