Use of Artificial Neuron Network to Predict Dental Arch Form

ABSTRACT

Objective: To develop diagnostic reference for arch form using Artificial Neuron Network (ANN) from tooth size and arch dimension variables on scanned-dental cast from patients with class I malocclusion treated orthodontically. Material and Methods: One hundred and ninety pairs of dental cast pre-post orthodontic treatment gathered from Orthodontic clinics were scanned and then all dimension variables were measured using Image Tool (gender, tooth size and arch dimension). The multivariate data were analyzed statistically using Stata (Lakeway Drive, College Station, Texas USA). The statistic results were compiled to build the neuron network software for analyzing arch form. Results: Gender and all variables from pre-treatment do not influence arch form. Intercanine width, canine depth, intermolar width, and molar depth are variables that influence arch form. The result of the statistical analyses can be used to develop software based on artificial neural network. Output program is the arch form, such as oval, square or tapered. The software can describe arch form with the accuracy of 76.31%. Conclusion: A software using Artificial Neuron Network to describe arch form can be used for diagnostic reference to Class I malocclusion.