ABSTRACT
In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.
ABSTRACT
There have been many radiographic studies on age estimation that evaluate reduction in size of dental pulp cavity with secondary dentin formation. The Paewinsky method reported high accuracy in estimating ages by measuring the width of the pulp cavity in panoramic radiographs. The aim of this study was to evaluate the application of the Paewinsky method to digital periapical radiographs. This study was conducted on 103 cases that reported to the Section of Human Identification of the National Forensic Service. The age was calculated by applying the Paewinsky method that measures the root and pulp canal width at three points in a tooth. The estimation results were compared with those calculated by the Johanson method. When the Paewinsky models were applied to digital periapical radiographs, the errors were significantly greater as compared to the original study. The errors of the maxillary second premolar and mandibular lateral incisor were greater than those of the maxillary central incisor, lateral incisor, mandibular canine, and first premolar. Furthermore, errors of the age estimation models in level C were greater than those in levels A and B. This study could be a reference for the application of the Paewinsky method to digital periapical radiographs.
Subject(s)
Age Determination by Teeth , Bicuspid , Dental Pulp Cavity , Dentin, Secondary , Forensic Anthropology , Incisor , Methods , Radiography, Dental , ToothABSTRACT
There have been many radiographic studies on age estimation that evaluate reduction in size of dental pulp cavity with secondary dentin formation. The Paewinsky method reported high accuracy in estimating ages by measuring the width of the pulp cavity in panoramic radiographs. The aim of this study was to evaluate the application of the Paewinsky method to digital periapical radiographs. This study was conducted on 103 cases that reported to the Section of Human Identification of the National Forensic Service. The age was calculated by applying the Paewinsky method that measures the root and pulp canal width at three points in a tooth. The estimation results were compared with those calculated by the Johanson method. When the Paewinsky models were applied to digital periapical radiographs, the errors were significantly greater as compared to the original study. The errors of the maxillary second premolar and mandibular lateral incisor were greater than those of the maxillary central incisor, lateral incisor, mandibular canine, and first premolar. Furthermore, errors of the age estimation models in level C were greater than those in levels A and B. This study could be a reference for the application of the Paewinsky method to digital periapical radiographs.