Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery

Abstract

Diagnosis and treatment planning are the most important steps in the orthognathic surgery for the successful treatment. The purpose of this study was to develop a new artificial intelligent model for surgery/non-surgery decision and extraction determination, and to evaluate the performance of this model. The sample used in this study consisted of 316 patients in total. Of the total sample, 160 were planned with surgical treatment and 156 were planned with non-surgical treatment. The input values of artificial neural network were obtained from 12 measurement values of the lateral cephalogram and 6 additional indexes. The artificial intelligent model of machine learning consisted of 2-layer neural network with one hidden layer. The learning was carried out in 3 stages, and 4 best performing models were adopted. Using these models, decision-making success rates of surgery/non-surgery, surgery type, and extraction/non-extraction were calculated. The final diagnosis success rate was calculated by comparing the actual diagnosis with the diagnosis obtained by the artificial intelligent model. The success rate of the model showed 96% for the diagnosis of surgery/non-surgery decision, and showed 91% for the detailed diagnosis of surgery type and extraction decision. This study suggests the artificial intelligent model using neural network machine learning could be applied for the diagnosis of orthognathic surgery cases.