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Objectives: specific morphologic aspects of the temporomandibular joint (TMJ) on CBCT could be considered as an identifier for human identification. The aim of the current study was to quantify the potential of specific measures from TMJ on CBCT images, separate or combined, for subject identification. Methods: CBCTs of TMJ of 198 Slavic and Eastern origin patients were examined to find out which parameters of the joint were potential identifiers. Evaluation of parameters was performed using ICATVision and GIMP software. One representative slice where measurements were performed, for both planes, was chosen. Afterwards, nine landmarks on sagittal and 6 landmarks of frontal planes, for right and left sides simultaneously, were established. Width, height, length of TMJ were measured between the landmarks. Also, two angles were allocated. In total, 14 parameters were measured on the sagittal plane and 4 on the frontal plane. Whereupon, statistical analysis was done. At first step; sex and side were analyzed separately (univariate analysis) to find out if there is a significant difference between the left and right side and significant difference between males and females. In the next step; interaction between sex and sides were evaluated (multivariate analysis). Results: Periost density showed to be the most unique single measurement. The side of the joint does not make difference for the identifying purposes.There is no significant difference between right and left sides, however, there is a considerable (p<0,0001) impact of sex on some of the variables. When it comes for a combination of the variables, the most unique patterns of identifiers (12,12%) showed 26 parameters together. Conclusion: Considering discussed outcomes, the present study indicated the possibility for obtaining information from morphologic identifiers like TMJ. By combining 26 parameters of the joint it is possible to garner additional information which can be used for identification purposes in forensic practise.

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Introduction: In forensic context, age estimation in adolescents and young adults is essential to prove whether or not the individual is a minor or an adult. The study aim is to systematically review the literature regarding the age prediction performances of dental age estimation methods based on the development of one or more third molars combined. The set research question is: “What is the most accurate dental age estimation method based on third molar(s) development? Materials and methods: This systematic review was performed according to criteria set forward by PRISMA statement. An electronic search until 03/03/2020 was performed in 4 databases: MEDLINE/PubMed, Cochrane library, EMBASE and Web of Science. Results: 113 studies were considered eligible for inclusion after review of title/abstract, full-text and reference list. Since we only discussed the point prediction and related uncertainty aspect, the age prediction performance and outcomes reported as the error, mean error, mean absolute error, RMSE (root mean squared error), R2 and others were included and classified. The ultimate data extracted for analysis were classified in 3 tables (respectively based on a validation sample): a) mean error (chronological age- estimated age) +- standard deviation, mean absolute error and R*2 number=6, b）regression formula +- standard deviation and R*2 number=11. c) regression model + root mean squared error and R*2 number=5. Discussion: The staging technique turned to be the most accepted registration predictor status compared to others, maybe due to the early origin and many developed branches. And it’s obvious that Demirjian et al. 1973 and Köhler Gleiser and Hunt 1955(modified) outperformed the others. Correspoundingly, the regression formula, linear regression models and Bayesian models were broad used to calculate the estimated ages as age estimation methods. a) The Demirjian et al. method performed better (José 2005) compared to others, especially with low ME(0.07-0.1)+S.D(1.22-1.23), MAE(0.074-0.106) and high R*2(0.45-0.54) and QA score (10) in a large Spanish subadult population. b) The Iranians turned out to have better result in 4TM and 2TM. By the Köhler Gleiser and Hunt 1955(modified) staging technique, they gained a low standard deviation and a high R*2 together with high QA score 9 for the tooth position examined in the study. And in general, the Belgian population performed best in 3TM examined. While for 1 TM, Thais population gained the lowest S.D (M: 1.29-1.3 F: 1.47-1.48) and a extremely high R*2 (M: 0.892 F: 0.85-0.86) using Demirjian technique. c) The Brazilians were found having better scores in RMSE 0.74-1.56 for both genders in linear regression models. Conclusion: In forensic practice, it is difficult to select a method for age estimation based on the development of third molars. In current systematic review, the Demirjian et al.1973(both technique and method)provided the most accurate point prediction of age in Spanish population, Köhler Gleiser and Hunt 1955(modified) technique + regression formula method did the most accurate prediction in Iranians population(4TM and 2TM), Demirjian technique + regression formula outperformed others in Thais population (1TM), and Köhler Gleiser and Hunt 1955(modified) technique + linear regression model method performed best in Brazilians, while more research is needed to verify these methods in the future.

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Age is an important feature of a person. Sometimes this information is not available. This is possible when you deal with migration. Because The official documentation is not available. Or a corpse where there is no identification available. In this case, a method is needed to determine the age. There exist methods to determine the age based on teeth information. However, these method’s rely on manual staging of the teeth which is time consuming. In this thesis, an automatic approach is researched to determine the age of a person. In this approach, you try to predict the age based on dental X-ray images. The proposed method tries to solve this problem using convolutional neural networks. This neural network is trained on a dataset of 4000 pictures of people between the ages of 8 and 25 year. The thesis focuses on preprocessing the data, selecting the best possible neural net architecture and finetuning the hyperparameters to get the best result possible. In the end, the proposed method was evaluated using 5-fold cross validation. The result is an MAE is 089 years and the MSE is 1.45 years. These results are very competitive towards the already available methods.

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Aim: The aim of this study was to evaluate the age prediction performance when the third molar information is added to permanent teeth in children and subadults from western Indian population. Material & Methods: A sample of 1533 orthopantomograms of Indian males (M) and females (F), in the age range between 2 and 23.99 years were collected retrospectively. The Demirjian staging technique was used on the lower left permanent teeth, and the technique of Gleiser et al modified by Köhler et al. was applied to stage all present third molars. Continuation ratio models were obtained for permanent teeth (PT), third molars (TM), permanent teeth and third molars combined (PT + TM) as predictors and age as the response. Mean error (ME;), Mean absolute error (MAE), Root mean squared error (RMSE), Coverage and width prediction intervals were calculated from the models. Results: When third molar information was added to permanent teeth, the decrease in mean error (ME) was 0.10 years in M and 0.13 years in F. Whereas, the decrease in Mean absolute error (MAE) was 0.36 years (4.4 months) in M and 0.43 years (5.2 months) in F. An overall, decrease in root mean squared error (RMSE) of 0.52 years (6.3 months) in males and females both. The highest decrease in RMSE was noted for the age category of 14 to 14.99 years in males (1.41 years) and between 23 to 23.99 years in females (1.67 years) when PT+TM model was compared to PT model. Conclusion: Inclusion of the third molar developmental information to permanent teeth does improve the age prediction performance in Indian children and subadults. Especially in the age groups of 13 to 14.99; 20 to 22.99 and 11 to 16.99; 21 to 23.99 for males and females respectively.

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Third molar development is used to estimate age in sub adults. However, the incidence of third molar impaction is high. If third molars are impacted, their development may be disturbed and may have consequences on the performed age estimations. The aim of current study was to evaluate the influence of third molar impaction related parameters on third molar development and related age estimations. Impaction related parameters such as contact between third and corresponding second molar, retro-molar space availability, and angulation between third and corresponding second molar were examine on 5.279 panoramic radiographs from 1,031 subjects (473 male, 558 female) with ages ranging between 3.2 and 23.5 years. Impaction status was assigned by the evaluation of contact with the corresponding second molar and the angulation measurement between the long axes of third and second molar (<-22 or >18 for the upper third molar and <-17 or >12 for the lower third molar). All present third molars were staged according to modified Köhler et al. staging technique. Significant differences were found in mean age as function of presence of contact and retro-molar space availability, depended on third molar position and on stage. There were significant linear relation between angulation and age, depended on stages. There was significant different in age between impacted and non-impacted third molars. However, all the differences were clinically very small (≤0.65 years). Impaction related parameters did not meaningfully influence third molar development. Therefore impacted third molars can be examined for age estimation purposes equally as non-impacted third molar.