Background The study highlights the gonial angle as a key craniofacial landmark for age and gender determination in forensic cases. It emphasizes population-specific analysis, enhancing precision by recognizing variations between populations. By clarifying the gonial angle\'s forensic use, the study offers clear guidelines, improving forensic practices. Moreover, the gonial angle and age and gender correlations are thoroughly examined, offering important information on their forensic relevance. The results highlight how crucial population-specific research is to improving the precision and dependability of forensic age and gender estimation techniques, which advances forensic anthropology and supports forensic investigations around the globe. Aim and objective The purpose of this study is to assess the accuracy of age and gender estimates using gonial angles. The objectives of this research are to evaluate the precision of age and gender estimates utilizing the gonial angle. Materials and methods This present study comprises two groups based on age groups: Group I belongs to 51 to 60 years of age, and Group II belongs to 61 to 70 years of age. Making use of G-Power software (version 3.1.9.4, Düsseldorf, Germany), the sample size was determined. The calculation ensured 95% statistical power at a significance level (alpha error probability) of 0.05. To achieve sufficient statistical power, a total of 1000 samples were included, with a projected required sample size of 92. A total of 1000 samples, consisting of 500 male and 500 female panoramic radiographs, were meticulously selected for the study. The samples picked were within the age range of 51 to 70 years. Orthopantomograms were determined using Planmeca software (Planmeca Romexis®, Version 6.0, USA Inc.). Descriptive statistics, including prediction classification analysis of age and gender, were conducted using SPSS Statistics version 16.0 (SPSS Inc., Released 2007, SPSS for Windows, Version 16.0, Chicago, SPSS Inc.). Results According to this study, the mean gonial angle of males aged 51 to 60 years is larger (124.7370 degrees) than that of females (119.6371 degrees). The female group\'s mean estimates are more accurate, as seen by the smaller standard error (0.20844) compared to the male group\'s (0.60998). A statistically significant difference in mean gonial angles between the genders is evident, with males having a larger gonial angle (p-value <0.001). In the age range of 61 to 70 years, the mean gonial angle of females is higher (128.4322 degrees) than that of males (124.0529 degrees). In this instance, the male group\'s standard error is smaller (0.14968) than the female group\'s (0.30028), indicating more accurate mean estimates. Once more, a statistically significant difference is indicated by a p-value of less than 0.001, with females having a larger gonial angle than males. Conclusion Our study revealed that the gonial angle of the mandible can be considered a reliable parameter for gender identification. The study\'s limitation is its inability to reliably identify gender in the subadult population and in cases of edentulousness. An orthopantomogram is a trustworthy and accurate method for taking the different measurements needed to identify the gender of a particular mandible.

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

Cameriere developed a method on orthopantomograms (OPG) to assess adult age of 18 years based on the relationship between age and the third molar maturity index I3M. The aim of this study was to evaluate whether Cameriere\'s method could be applied to computed-tomography scans (CT-scans) from a population of French juveniles and young adults and compare the results obtained from OPG of the same individuals. Our sample comprised 200 examinations that had been performed at the radiological department of a French University hospital between 2007 and 2020. Each patient had received an OPG and a cranial CT scan for medical purposes, and we used a similar adaptation of I3M based on OPG to determine the I3M based on CT scans. Due to exclusion criteria, our final sample comprised 71 OPGs and 63 CT scans. Based on the 71 OPGs, there was concordance between chronological age and estimated age, with a sensitivity of 78.57%, a specificity of 89.47%, and a misclassified rate of 18.03% based on tooth 38, and a sensitivity of 78.79%, a specificity of 91.67%, and a misclassified rate of 17.78% based on tooth 48. Our results based on CT scans presented concordance between chronological age and estimated age for tooth 38 described by a sensitivity of 77.78%, a specificity of 94.12%, and a misclassified rate of 16.98%. The concordance between chronological age and estimated age based on 48 had a sensitivity of 75.00%, a specificity of 93.75%, and a misclassified rate of 19.23%. The > 90% ICC indicate an excellent similarity between measurements of teeth 38 and 48 based on OPGs and CT scans. This study has revealed the applicability of the Cameriere\'s method to calculate the I3M based on CT scans from a French population. The results based on CT scans are similar to results based on OPGs from the same individuals.

In forensic practice, medicolegal physicians are often tasked with estimating age using dental evidence. This calls for an uncomplicated, reliable, and reproducible method for dental age estimation, enabling physicians to proceed without specific odontological expertise. Among various dental methods, third molar eruption analyses are less complicated and easier to perform. In our study, we explored the effectiveness of Gambier et al.\'s scoring system, which examines the eruption of all third molars. We retrospectively analysed 1032 orthopantomograms (528 males and 504 females) of individuals aged between 15 and 24 years. The mean chronological age increased with the progression of stages (1 to 3) and phases (A to D) of the third molar eruption for both sexes. In terms of stages, none showed significant discrimination between minors (<18 years) and adults (>18 years), especially for males. However, Gambier\'s phase D displayed a relatively high likelihood of being 18 years or older, with an overall 85.9 % of males and 95.7 % of females having all third molars in stage 3 being 18 years or older. While the tested method could be helpful in indicating the completion of the 18th year of life, caution is advised (due to a high percentage of false positives), and it should be used alongside other age assessment methods by experts.

Dental age estimation has its application in various subdisciplines of medicine and dentistry. New methods of dental age (DA) estimation are emerging and it is important that we compare different methods to determine which one is more closely related to the chronological age. Demirjian\'s method is one of the most widely used techniques and has been tested in various ethnic populations globally. In 2016, another approach to DA estimation is the London atlas of human tooth development and eruption. No study has compared Demirjian\'s comprehensive chart and London atlas method in the Indian population. Hence, in the current study, we estimated DA using Demirjian\'s comprehensive chart and London atlas method for association with the known chronologic age in children and adolescent population. The study also attempted to determine if sexual dimorphism existed in DA estimated by the two methods. Estimation was performed for both methods on 100 orthopantomogram records (50 males and 50 females, aged 6-16 years) of orthodontic patients. The data were compared and analysed using paired t-tests. There was an overestimation of DA by Demirjian\'s comprehensive chart on an average of +1.3 years in males and +0.5 years in females, whereas using London atlas, it was +1.4 years in males and +0.5 years in females. The mean of underestimation was -0.6 years in males and -0.8 years in females using Demirjian\'s comprehensive chart, whereas it was -0.8 years in males and -0.5 years in females. A statistically significant difference (P < 0.0001) was found when mean chronological age (11.6 ± 2.6) years of the participants was compared with DA estimated using either Demirjian\'s comprehensive chart (12.3 ± 2.8) years or London atlas (11.8 ± 2.9) years. The trends in this pilot study point towards more accuracy of London atlas over Demirjian\'s method when done using comprehensive chart for estimating DA. In summary, the results of the current pilot study indicates greater accuracy of London atlas method over Demirjian\'s comprehensive chart method for estimating DA in Indian population. This finding should be validated by conducting similar studies using larger sample, on diverse Indian ethnic populations, for applicability in pedodontic, orthodontic, and forensic domains.
UNASSIGNED: No study has compared Demirjian\'s comprehensive chart and London atlas method in the Indian population.The dental age estimated by London method was closer to chronological age as compared to Demirjian\'s method in our study sample.Significant difference was found in chronological age and estimated age using London atlas method in both males and females.

This work aimed to assess the pulp/tooth area ratio\'s utility in the upper central incisors using orthopantomograms. A convenience sample of 801 adult patient orthopantomograms was studied. Image J® software was used to measure the pulp/tooth area ratio, and a regression model was developed. Our results conclude that the methodology assessing upper incisors\' pulp/tooth area ratio using orthopantomograms can lead to age overestimation and statistically significant differences between chronological and estimated age. For those over 50, no correlation between pulp/tooth area ratio and chronological age was found, suggesting that this may be the upper limit of this technique in this population. This methodology may not be suitable for age estimation, particularly in older adults.

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes.

Different studies have established that the mineralization stages of the second mandibular molar can be used in forensic age estimation. Nowadays, the estimate\'s accuracy is an ethical concern, producing as few false positives (individuals incorrectly classified as older than a determined threshold) and false negatives (individuals incorrectly classified as younger than a determined threshold) as possible. Some have hypothesized that changes in teeth number may influence tooth mineralization, altering the age estimate process. This paper analyzes whether third molar agenesis affects the second mandibular molar mineralization time frame. To do so, 355 orthopantomograms were evaluated for third molar agenesis, and the second mandibular molar mineralization stage was assessed using the Demirjian stages. Student\'s t-test was used to compare the difference in the mean age at which the various stages of 37 mineralization were reached in the groups with and without third molar agenesis. The level of statistical significance was set at 5%. The results pointed to a delay in second mandibular molar mineralization in the case of agenesis, suggesting the need to consider this when estimating age using dental techniques.

Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into \'under-18\' and \'over-18\' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.

UNASSIGNED: Age estimation using regressive alterations such as root dentin translucency, PDL attachment, and attrition are easy and reliable way of predicting the age. However, extensive and population-specific formula has not been generated. This attempt was to assess the correlation of these alterations with age and to generate a Linear regressive formula, specific to this population.
UNASSIGNED: Three alterations were assessed such as dental attrition, root dentin translucency, and periodontal attachment level from the extracted teeth. Dental attrition was measured using Johanson\'s and Li and Ji criteria. PDL attachment level and root dentin translucency was measured using the Johanson method and the Lamendin method.
UNASSIGNED: SPSS software (Version 27), Pearson correlation test, and Linear regressive analysis were used.
UNASSIGNED: Our results showed all three factors/parameters such as attrition, periodontal ligament, and translucency having a very good correlation with age and correlation coefficient r value ranging from 0.6 to 0.8. All the parameters were having statistically significant correlation with P value <0.005. Among them, root dentin translucency with Johanson G method showed excellent correlation with r = 0.83 followed by PDL attachment by Johanson G method with r = 0.702.
UNASSIGNED: Regressive changes such as Dentin translucency, PDL attachment and attrition on Coastal Karnataka showed a very good correlation with age. Among them, Dentin translucency by Johanson G method had the best correlation with the a standard error of estimate (SEE). Results of our study indicates that all these parameters [Translucency, PDL attachment, and attrition] can be utilized in age estimation.