Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: A scope and performance analysis

Abstract

To compare the Artificial Intelligence based model & conventional technique for prediction of extraction in orthodontic treatment plan. A comparative study was conducted on total 700 patients, who were divided into training set and testing set based on simple random sampling by means of computer generated random numbers. The photographs of the 630 patients [training set] along with the treatment plan finalized for them based on Arch Perimeter & Carey’s Analysis, was fed in the AI model [convolutional neural network (ResNet-50)] in order to train it for the stipulated function of eventually predicting the treatment plan in the testing set [70 patients], based on the input of the right profile photographs. The accuracy of measurement of the parameters of these seventy test set patients by the machine learning model relative to the manual method was compared eventually. Using the Statistical Package for Social Sciences, the acquired data was statistically analyzed, and p <0.05 was deemed statistically significant. The normality of the data was examined using the Shapiro-Wilks test and the Kolmogorov-Smirnov test. Depending on the collected data and normality assessed, appropriate reliability was estimated. The analysis of 70 test patients showed that 65.12% of the total extraction cases and 62.96% of the total non-extraction cases (as predicted by the AI model) were in agreement with the results of the model analysis.It is suggested that the present AI model can further be developed in order to improve the accuracy of prediction.