Multilayer Perceptron to Assess the Impact of Anatomical Risk Factors on Traumatic Dental Injuries: An Advanced Statistical Approach of Artificial Intelligence in Dental Traumatology

Abstract

Introduction: Traumatic dental injuries (TDIs) are the public dental health concern, with variable prevalence reported worldwide. Although, TDI is not a disease rather, it is a result of various risk factors. This study was performed to assess the influence of anatomical risk factors such as accentuated overjet, overbite, molar relationship, and lip competency in determining the number of traumatized teeth per affected individual by using the advanced statistical method of multilayer perceptron (MLP) model of deep learning algorithm of artificial intelligence (AI). Materials and Methods: A cross-sectional study consisted of 1000 school children (boys and girls) of index age groups between 12 and 15 years selected through multistage sampling technique. Orofacial anatomical risk factors associated with TDI were statistically analyzed by MLP model of deep learning algorithm of AI using IBM SPSS Modeler software (version 18, 2020). Results: MLP method revealed results in terms of normalized importance as overbite (100%) was the strongest risk factor for the occurrence of TDI in number of teeth of affected participants, followed by molar relationship (90.2%), overjet (87.7%), and the lip competency was found as the weakest risk factor. Conclusion: Using the MLP as statistical method, overbite was found as the strongest anatomical risk factor in determining the number of traumatized teeth per affected individual as compared to molar relationship, overjet, and lip competence.