The Reliability of Artificial Neural Network in Locating Minor Apical Foramen: A Cadaver Study

Abstract

Introduction

The purpose of this study was to evaluate the accuracy of the artificial neural network (ANN) in a human cadaver model in an attempt to simulate the clinical situation of working length determination.

Methods

Fifty single-rooted teeth were selected from 19 male cadavers ranging in age from 49–73 years. Access cavities were prepared, a file was placed in the canals, and the working length was confirmed radiographically by endodontists. The location of the file in relation to the minor apical foramen was categorized as long, short, and exact by the ANN, by endodontists before extraction, and stereomicroscopically after extraction. The results were compared by using Friedman and Wilcoxon tests. The significance level was set at P <.05.

Results

The Friedman test revealed a significant difference among groups (P < .001). There were significant differences between data obtained from endodontists and ANN (P = .001) and data obtained from endodontists and real measurements by stereomicroscope after extraction (P < .002). The correct assessment by the endodontists was accurate in 76% of the teeth. ANN determined the anatomic position correctly 96% of the time. The confidence interval for the correct result was 64.16–87.84 for endodontists and 90.57–101.43 for ANN.

Conclusions

ANN was more accurate than endodontists' determinations when compared with real working length measurements by using the stereomicroscope as a gold standard after tooth extraction. The artificial neural network is an accurate method for determining the working length.