Age estimation using canine pulp volumes in adults:A CBCT image analysis

Secondary dentine deposition is responsible for the decrease in the volume of the pulp cavity with age. Therefore, the volume of the pulp cavity can be considered as a predictor for estimating age. The aims of this study were to investigate the relationship strength between canine pulp volumes and chronological age from homogenous (approximately equal numbers of individuals in each age range) age distribution and to assess the effect of sex as predictor in age estimation. This study was performed on 719 subjects of Pakistani origin. Cone beam computed tomography images of 521 left maxillary and 681 left mandibular canines were collected from 368 females and 349 males aged from 15 to 65 years. Planmeca Romexis® software was used to trace the outline of the pulp cavity and to calculate pulp volumes. Regression analysis was performed to assess the correlation between pulp volumes considering with and without sex as a predictor with chronological age. The obtained results showed that mandibular canine pulp volume and sex have the highest predictive power (R 2 = 0.33). The relationship between mandibular canine pulp volume and sex with chronological age demonstrates an odd S-shaped non-linear relationship. A statistically significant difference in volumes of pulp was found (p = 0.000) between males and females. The conclusion was that predictions using the pulp volume of the mandibular canine and sex produced the best estimates of chronological age. </p

Multi-label Classification of Panoramic Radiographic Images Using a Convolutional Neural Network

Dentistry is one of the areas which mostly present potential for application of machine learning techniques, such as convolutional neural networks (CNNs). This potential derives from the fact that several of the typical diagnosis methods on dentistry are based on image analysis, such as diverse types of X-ray images. Typically, these analyses require an empiric and specialized assessment by the professional. In this sense, machine learning can contribute with tools to aid the professionals in dentistry, such as image classification, whose objective is to classify and identify patterns and classes on a set of images. The objective of this current study is to develop an algorithm based on a convolutional neural network with the skill to identify independently six specific classes on the images and classify them accordingly on panoramic X-ray images, also known as orthopantomography. The six independent classes are: Presence of all 28 teeth, restoration, braces, dental prosthesis, images with more than 32 teeth and images with missing teeth. The workflow was based on a DOE (Design of experiments) study, considering the neural network architecture variables as factors, in order to identify the most significant ones, which ones mostly contribute to improve the fitness of the network, and the interactions between these in order to optimize the network architecture, based on the F1 and recall scores. Obtained results are promising, considering that for the optimal network architecture, F1 and Recall scores of 87% and 86%, respectively, were obtained.São Paulo State University (UNESP)São Paulo State University (UNESP

Maximum intensity projection of cranial computed tomography data for dental identification

Dental radiographs play the major role in the identification of victims in mass casualties besides DNA. Under circumstances such as those caused by the recent tsunami in Asia, it is nearly impossible to document the entire dentition using conventional x-rays as it would be too time consuming. Multislice computed tomography can be used to scan the dentition of a deceased within minutes, and the postprocessing software allows visualization of the data adapted to every possible antemortem x-ray for identification. We introduce the maximum intensity projection of cranial computed tomography data for the purpose of dental identification exemplarily in a case of a burned corpse. As transportable CT scanners already exist, these could be used to support the disaster victim identification teams in the field

Estimation of pediatric skeletal age using geometric morphometrics and three-dimensional cranial size changes

International audienc