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Objectives: This study examines the relationship between chronological age and the ratio of pulp chamber volume to crown volume in maxillary central incisors and first molars. Cone-beam computed tomography (CBCT) is used to measure the pulp chamber size, which decreases following the eruption of the tooth. Study design: The study focused on age estimation using cone beam computed tomography (CBCT) images of 156 patients from Mashhad Dental School (67 maxillary central incisors and 42 maxillary first molars). We stratified the age range into three groups: 18–27 years, 28–37 years, and 38–49 years. The study used Planmeca ProMax 3D and ITK-SNAP 3.4.0 software for segmentation and volume quantification. The data was analyzed using PASW Statistics 18 software, with descriptive statistics and linear regression analysis for age estimation. Results: This study created a mathematical model for calculating chronological age, taking into account factors such as pulp-to-crown volume ratio, gender, and tooth type. Results showed no significant difference in mean age between male and female subjects, and the average pulp-to-crown volume ratio remained consistent across genders and tooth types. Conclusion: The study uses CBCT to estimate Iranian population age using maxillary central incisors and first molars. The pulp chamber to crown volume ratio is reliable for 28–37 years, but accuracy decreases with age. Larger sample sizes and other teeth could improve estimation.

Scoping reviews in forensic odontology and dental anthropometry provide valuable information. Several current studies indicate different three-dimensional (3D) scanning and digital technologies in clinical dentistry. However, there is a paucity of literature on the utilization and performance of 3D scanner technologies in forensic science and/or medico-legal settings. This study undertook a scoping review of the 3D technologies utilized while researching the applied anatomy of the dentition of modern human forensics. The objectives of this study were to identify which 3D technologies are/were used to determine best practices in usage for research of the dentition of humans and to identify the gaps and challenges faced in the utilization of 3D technologies in dental research covering the forensic domains. Twenty-four key phrases were used in this study to search four databases for the period 1973 to 2023. The initial search yielded 6782 articles, which was then reduced to 1043 after removing any duplicate articles. After title and abstract screening, 35 articles were featured, this was further reduced to 24 articles after applying the selection criteria. The results highlighted the following gaps and challenges faced in the utilization of 3D technologies in dental forensic research: the lack of utilization of 3D equipment for forensic application, as no studies were noted to have been conducted within an African context; a disparity between studies in the Northern and Southern hemispheres, with many studies concentrated in the European and Far East regions and a lack of 3D resources in Africa coupled with the lack of specialized personnel trained to utilize the equipment if available optimally. The scanning modality frequently appears is the 3D Cone Beam Computed Tomography (3D CBCT). Intraoral scans and 3D scanning of dental casts coupled with 3D Geometric Morphometrics were also noted to have been used.

Abstract Forensic age assessment in the living can provide legal certainty when an individual’s chronological age is unknown or when age-related information is questionable. An established method involves assessing the eruption of mandibular third molars through dental panoramic radiographs (PAN). In age assessment procedures, the respective findings are compared to reference data. The objective of this study was to generate new reference data in line with the required standards for mandibular third molar eruption within a German population. For this purpose, 605 PANs from 302 females and 303 males aged 15.04 to 25.99 years were examined. The PANs were acquired between 2013 and 2020, and the development of the mandibular third molars was rated independently by two experienced examiners using the Olze et al. staging scale from 2012. In case of disagreement in the assigned ratings, a consensus was reached through arbitration. While the mean, median and minimum ages were observed to increase with each stage of mandibular third molar eruption according to the Olze method, there was considerable overlap in the distribution of age between the stages. The minimum age for stage D, which corresponds to complete tooth eruption, was 16.1 years for females and 17.1 years for males. Thus, the completion of mandibular third molar eruption was found in both sexes before reaching the age of 18. In all individuals who had at least one tooth with completed eruption and who were younger than 17.4 years of age (n = 10), mineralization of the teeth in question was not complete. Based on our findings, the feature of assessing mandibular third molar eruption in PAN cannot be relied upon for determining age of majority.

Practically, when only parts of an unidentified human body are found, age estimation with limited materials is required. The purpose of this study was to investigate methods for estimating age using the uterus and ovaries. Among forensic autopsies performed between January 2011 and March 2022, 211 uteruses and 521 ovaries of 322 women were used for this study. Measured values for ovarian weight and uterine volume index were corrected by body surface area to consider the effect of body size. The corrected uterine volume index increased in teenage years and achieved its maximum in the 40–49 group, then gradually decreased with increasing age. The corrected ovarian weight increased until the twenties, after which it decreased with age. For women aged 20 years or more, receiver operating characteristic (ROC) curve analysis suggested that a uterine volume index of 41.2 cm3/m2 was the cutoff value for classifying the age as ≥60 years or 2 and 1.92 g/m2 were the cutoff values for classifying the age as ≥40 years or

Objective To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation （MPR）, and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. Methods The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction （VR） and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures （both left and right） and lambdoid sutures （both left and right） were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation （gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression） were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. Results Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. Conclusion The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Objective To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older. Methods A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics. Results Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old. Conclusion It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging （MRI） in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.

Objective To investigate the application value of combining the Demirjian’s method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian’s method. Various machine learning algorithms, including support vector regression （SVR）, gradient boosting regression （GBR）, linear regression （LR）, random forest regression （RFR）, and decision tree regression （DTR） were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error （MAE） of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Objective To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. Methods The retrospective pelvic CT imaging data of 1 200 samples （600 males and 600 females） aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error （MAE） and root mean square error （RMSE） were used as primary indicators to evaluate the model. Results Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. Conclusion The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age.

Objective To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography （CBCT） images, and to compare and analyze the estimation results. Methods A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms （K-nearest neighbor, ridge regression, and decision tree） and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. Results The K-nearest neighbor model （R2=0.779） and the ridge regression model （R2=0.729） outperformed stepwise regression （R2=0.617）, while the decision tree model （R2=0.494） showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. Conclusion Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Forensic age assessment in the living can provide legal certainty when an individual's chronological age is unknown or when age-related information is questionable. An established method involves assessing the eruption of mandibular third molars through dental panoramic radiographs (PAN). In age assessment procedures, the respective findings are compared to reference data. The objective of this study was to generate new reference data in line with the required standards for mandibular third molar eruption within a German population. For this purpose, 605 PANs from 302 females and 303 males aged 15.04 to 25.99 years were examined. The PANs were acquired between 2013 and 2020, and the development of the mandibular third molars was rated independently by two experienced examiners using the Olze et al. staging scale from 2012. In case of disagreement in the assigned ratings, a consensus was reached through arbitration. While the mean, median and minimum ages were observed to increase with each stage of mandibular third molar eruption according to the Olze method, there was considerable overlap in the distribution of age between the stages. The minimum age for stage D, which corresponds to complete tooth eruption, was 16.1 years for females and 17.1 years for males. Thus, the completion of mandibular third molar eruption was found in both sexes before reaching the age of 18. In all individuals who had at least one tooth with completed eruption and who were younger than 17.4 years of age (n = 10), mineralization of the teeth in question was not complete. Based on our findings, the feature of assessing mandibular third molar eruption in PAN cannot be relied upon for determining age of majority. [ABSTRACT FROM AUTHOR]

Age assessment of migrants is crucial, particularly for unaccompanied foreign minors, a population facing legal, social, and humanitarian challenges. Despite existing guidelines, there is no unified protocol in Europe for age assessment. The Forensic Anthropology Society of Europe (FASE) conducted a comprehensive questionnaire to understand age estimation practices in Europe. The questionnaire had sections focusing on the professional background of respondents, annual assessment numbers, requesting parties and reasons, types of examinations conducted (e.g., physical, radiological), followed protocols, age estimation methods, and questions on how age estimates are reported. The questionnaire's findings reveal extensive engagement of the forensic community in age assessment in the living, emphasizing multidisciplinary approaches. However, there seems to be an incomplete appreciation of AGFAD guidelines. Commonalities exist in examination methodologies and imaging tests. However, discrepancies emerged among respondents regarding sexual maturity assessment and reporting assessment results. Given the increasing importance of age assessment, especially for migrant child protection, the study stresses the need for a unified protocol across European countries. This can only be achieved if EU Member States wholeheartedly embrace the fundamental principles outlined in EU Directives and conduct medical age assessments aligned with recognized standards such as the AGFAD guidelines. [ABSTRACT FROM AUTHOR]

Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human eyes. We hypothesized that the binocular RE metrics including sphere and cylinder power, axis orientation, and interpupillary distance (IPD) can be used for forensic age estimation of an unknown individual. RE data of both eyes were collected from the clinical optometric exams and prescription glasses of 2027 Egyptian individuals aged between 2 to 93 years. The differences between age groups as well as sides, and sexual dimorphism were explored. Two modeling methods were compared: multiple and stepwise linear regression (LR) versus machine learning Regression Forest (RFM). Data were apportioned into training and test datasets with a ratio of 80/20. The results showed significant differences among the age groups in each eye for all variables. Stepwise LR improved the results over models based on the one-sided lens due to selection of IPD in addition to the left and right axis, and left sphere as independent variables. For the RFM, the left axis and IPD were the most important features. RFM outperformed LR in terms of accuracy and root mean squared error (RMSE). The estimated age within ±10 years showed 81.4% accuracy rate and RMSE = 8.9 years versus 38.5% accuracy rate and RMSE = 17.99 years using RFM and stepwise LR, respectively, in the test set. The current study upholds the significance of the age-related changes of refractive error in formulating alternative forensic age estimation models when standard methods are unavailable. [ABSTRACT FROM AUTHOR]

Recent research observed 92% accuracy for age-at-death estimations by U.S. forensic anthropologists. The present study compares this case report level accuracy to method level accuracy for the most commonly used methods in U.S. casework, drawing from the Forensic Anthropology Database for Assessing Methods Accuracy (FADAMA). Method application rate (i.e., how often a method is used in casework) was analyzed for n= 641 cases and identified 15 methods with an application rate >45 cases, and the present study focused further analyses on these 15 methods. Of the 15, only four yielded accuracies greater than or equal to the 92% documented for case-report level accuracy. The other 11 methods produced accuracy rates ranging from 54% to 91%, with six of these below 70% This disconnect between highly accurate age estimations at the case report level compared to the poor performance at method level suggests that practitioner interpretation and synthesis of the methods' outcomes is a critical step for increasing the accuracy rates of the age estimations as reported on the final case report. This inference was further supported by the study's results which indicated that practitioner interpretations of frequently used method combinations improve accuracy and age range width of age estimation. The study also performed a Fisher's Exact test to assess whether case report-level accuracy differed with the number of aging methods used in a case, and found no significant differences. [ABSTRACT FROM AUTHOR]

Aging is a complex process influenced by genetic, epigenetic, and environmental factors that lead to tissue deterioration and frailty. Epigenetic mechanisms, such as DNA methylation, play a significant role in gene expression regulation and aging. This study presents a new age estimation model developed for the Turkish population using blood samples. Eight CpG sites in loci TOM1L1, ELOVL2, ASPA, FHL2, C1orf132, CCDC102B, cg07082267, and RASSF5 were selected based on their correlation with age. Methylation patterns of these sites were analyzed in blood samples from 100 volunteers, grouped into age categories (20-35, 36–55, and ≥56). Sensitivity analysis indicated a reliable performance with DNA inputs ≥1 ng. Statistical modeling, utilizing Multiple Linear Regression, underscores the reliability of the primary 6-CpG model, excluding cg07082267 and TOM1L1. This model demonstrates strong correlations with chronological age (r= 0.941) and explains 88% of the age variance with low error rates (MAE = 4.07, RMSE = 5.73 years). Validation procedures, including a trainingtest split and fivefold cross-validation, consistently confirm the model's accuracy and consistency. The study indicates minimal variation in error scores across age cohorts and no significant gender differences. The developed model showed strong predictive accuracy, with the ability to estimate age within certain prediction intervals. This study contributes to the age prediction by using DNA methylation patterns, which can have disparate applications, including forensic and clinical assessments. [ABSTRACT FROM AUTHOR]

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification. [ABSTRACT FROM AUTHOR]

Age is a key demographic in conservation where age classes show differences in important population metrics such as morbidity and mortality. Several traits, including reproductive potential, also show senescence with ageing. Thus, the ability to estimate age of individuals in a population is critical in understanding the current structure as well as their future fitness. Many methods exist to determine age in wildlife, with most using morphological features that show inherent variability with age. These methods require significant expertise and become less accurate in adult age classes, often the most critical groups to model. Molecular methods have been applied to measuring key population attributes, and more recently epigenetic attributes such as methylation have been explored as biomarkers for age. There are, however, several factors such as permits, sample sovereignty, and costs that may preclude the use of extant methods in a conservation context. This study explored the utility of measuring age‐related changes in methylation in candidate genes using mass array technology. Novel methods are described for using gene orthologues to identify and assay regions for differential methylation. To illustrate the potential application, African cheetah was used as a case study. Correlation analyses identified six methylation sites with an age relationship, used to develop a model with sufficient predictive power for most conservation contexts. This model was more accurate than previous attempts using PCR and performed similarly to candidate gene studies in other mammal species. Mass array presents an accurate and cost‐effective method for age estimation in wildlife of conservation concern. [ABSTRACT FROM AUTHOR]

Background: Age plays a vital role in determining a person's identity in legal matters, both during their lifetime and after their death. The objective of this study was to ascertain an individual's age by examining the fusion of the xiphoid process and manubrium with the sternum. Material and methods: A total of 112 cases were included in this study which is carried out as mid-term analysis. The cases were selected from the general public, including patients admitted to departments, their family, and police personnel attending the a tertiary care institution in Haryana. Radiographs of sternum lateral & AP view were taken. Data were analyzed using SPSS 21. Result: A total of 112 patients were enrolled. 64 (57.1%) were males and remaining were females. Regarding Xiphoid process, complete fusion was observed in 83% both males and females in age group 60 - 65 years. Complete fusion was observed in all male & female patients in 65 - 70 years. Regarding manubrium, complete fusion was observed in 75% males and 88.9% females in age group 50 - 55 years. Complete fusion was observed in most female patients in age group 55 - 60, 60 - 65 years and 65 - 70 years. Conclusion: The age can be assessed by examining the fusion of the xiphoid process and manubrium with the sternal body using radiographic examination. Moreover, the union of the xiphoid process and manubrium with the sternal body initiates after the age of 30 years, but full fusion taking place after the age of 60 years. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to compare the Cameriere's third molar maturity index and Olze et al.'s stages of radiographic visibility of the root pulp in estimating the age of maturity in the Turkish population. The age of majority, which is legally significant, marks the transition from childhood to adulthood. In Turkey, the age of majority is set at 18 years. As the third molars continue to develop at this age, they can serve as an indicator of dental age. Materials and Methods A total of 705 panoramic radiographs obtained from individuals aged 15 to 22 years, including children and adults, were included in this study. The left mandibular third molars were evaluated on panoramic radiographs using Cameriere's third molar maturity index and Olze's method of radiographic root pulp visibility (RPV) stages. Minimum and maximum values were noted for each stage, and a median with upper and lower quartiles, as well as mean and standard deviation were calculated. Sensitivity and specificity values were calculated. Results In males, Cameriere's third molar maturity index demonstrated a sensitivity of 0.77% and specificity of 0.96%, while in females, it showed a sensitivity of 0.57% and specificity of 0.92%. Regarding Olze et al.'s stage 0, the sensitivity and specificity values were 0.86% and 0.79% in males, and 0.85% and 0.75% in females, respectively. Conclusion Although both methods can be used to distinguish individuals below or above the age of 18, the cut-off value suggested by Cameriere's method resulted in a higher rate of type 2 error (false negativity). Therefore, the method proposed by Olze et al., based on the radiographic visibility of the root pulp, can be employed to differentiate between adults and minors in the Turkish population. [ABSTRACT FROM AUTHOR]

Individual age can be used to design more efficient and suitable management plans in both in situ and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8–23 selected CpG sites to build age estimation models using machine learning methods at only about $3–7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species. [ABSTRACT FROM AUTHOR]

The oral cavity can be explored as an area to identify the impacts of aging. Exfoliative cytology is a painless, rapid, and effective method for collecting intact cells from various epithelial layers for microscopic examination. Therefore, the current study's objective was to determine the average cell size using morphometric analysis and radiovisiography (RVG) to estimate an individual's age utilizing buccal smears and the pulp/tooth ratio. A software program was utilized to determine the cell size using exfoliative cytology, and the pulp and tooth area was measured using RVG of mandibular canine with the help of an image analysis software. Appropriate statistical tests were performed using IBM SPSS statistical software for Windows (version 25). The patient's chronological age, pulp area, and the associated pulp-tooth area ratio values were determined, along with Karl Pearson's correlation coefficient. It was discovered by using a paired t-test to compare chronological age to the age deduced from cell size and pulp-tooth area ratio. According to the current study, age estimation could be done based on average cell size, pulp size, and pulp-tooth area ratio for forensic odontology. Studies with bigger sample sizes and diverse population would help achieve definitive conclusions about the reliability of this particular method. [ABSTRACT FROM AUTHOR]

This study examined variations in spectral absorption and emission intensity of latent fingerprints over time, employing UV-Vis spectroscopy and photoluminescence spectroscopy. There was noticeable absorption at 296nm on the samples, and the fluorescence emission intensity showed variations. A total of 1600 samples from various categories were analyzed to validate this hypothesis. The emission intensity observed under photoluminescence spectroscopy varied over time, showing greater intensities in controlled female and male samples compared to the uncontrolled samples that were exposed to environmental conditions. The emission intensity of the controlled female (CF) sample was the highest at 6341168 CPS, followed by the controlled male (CM) sample at 1270240 CPS. On the other hand, the uncontrolled female (UF) sample exhibited an emission intensity of 5431940 CPS, while the uncontrolled male (UM) sample had an intensity of 100764 CPS. This emission intensity variation proved to be an efficient age profiling marker for the latent fingerprints. [ABSTRACT FROM AUTHOR]

Objective To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older. Methods A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics. Results Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old. Conclusion It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images. [ABSTRACT FROM AUTHOR]

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging (MRI) in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology. [ABSTRACT FROM AUTHOR]

Objective To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered. [ABSTRACT FROM AUTHOR]

Objective To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. Methods The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. Results Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. Conclusion The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice. [ABSTRACT FROM AUTHOR]

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results. [ABSTRACT FROM AUTHOR]

Objective To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. Methods A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. Results The K-nearest neighbor model (R² =0.779) and the ridge regression model (R² =0.729) outperformed stepwise regression (R² = 0.617), while the decision tree model (R² =0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. Conclusion Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research. [ABSTRACT FROM AUTHOR]

Objective To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. Methods The retrospective pelvic CT imaging data of 1 200 samples (600 males and 600 females) aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error (MAE) and root mean square error (RMSE) were used as primary indicators to evaluate the model. Results Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. Conclusion The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age. [ABSTRACT FROM AUTHOR]

The purpose of this study is to estimate chronological age and determine whether individuals were aged under or over 18 years using root pulp visibility (RPV) in cone-beam computed tomography (CBCT) images. The study included CBCT images of 699 individuals aged between 15 and 75 years. One thousand twenty-three mandibular second molar (2M) teeth were evaluated using Olze's RPV method in four stages. Descriptive statistics of the stages and the relationship between the stages and the chronological age were assessed. The distribution of the stages was analyzed according to the 18-year age threshold. There was a positive correlation between RPV stages and chronological ages in both sexes. For females and males, the mean ages of stage 0, stage 1, and stage 2, for females and males, were found as 27.21, 28.93, and 33.68 years, and 37.69, 40.9, and 44.88 years, respectively. Stage 0 and stage 1 were found both in individuals aged under and over 18 years, and stage 2 and stage 3 were not observed in individuals aged under 18 years. The presence of stage 2 and stage 3 may be an indication that an individual is aged over 18 years according to Olze's RPV age estimation method. For more reliable results, 2M teeth should be examined bilaterally in forensic science. [ABSTRACT FROM AUTHOR]

Human age estimation from trace samples may give important leads early in a police investigation by contributing to the description of the perpetrator. Several molecular biomarkers are available for the estimation of chronological age, and currently, DNA methylation patterns are the most promising. In this study, a QIAGEN age protocol for age estimation was tested by five forensic genetic laboratories. The assay comprised bisulfite treatment of the extracted DNA, amplification of five CpG loci (in the genes of ELOVL2 , C1orf132 , TRIM59 , KLF14 , and FHL2), and sequencing of the amplicons using the PyroMark Q48 platform. Blood samples from 49 individuals with ages ranging from 18 to 64 years as well as negative and methylation controls were analyzed. An existing age estimation model was applied to display a mean absolute deviation of 3.62 years within the reference data set. Key points Age determination as an intelligence tool during investigations can be a powerful tool in forensic genetics. In this study, five laboratories ran 49 samples and obtained a mean absolute deviation of 3.62 years. Five markers were analyzed on a PyroMark Q48 platform. [ABSTRACT FROM AUTHOR]

Background: The expression profiles of differentially expressed genes (DEGs) during pupal development have been demonstrated to be vital in age estimation of forensic entomological study. Here, using forensically important Aldrichina grahami (Diptera: Calliphoridae), we aimed to explore the potential of intrapuparial stage aging and postmortem interval (PMI) estimation based on characterization of successive developmental transcriptomes and gene expression patterns. Methods: We collected A. grahami pupae at 11 successive intrapuparial stages at 20 °C and used the RNA-seq technique to build the transcriptome profiles of their intrapuparial stages. The DEGs were identified during the different intrapuparial stages using comparative transcriptome analysis. The selected marker DEGs were classified and clustered for intrapuparial stage aging and PMI estimation and then further verified for transcriptome data validation. Ultimately, we categorized the overall gene expression levels as the dependent variable and the age of intrapuparial A. grahami as the independent variable to conduct nonlinear regression analysis. Results: We redefined the intrapuparial stages of A. grahami into five key successive substages (I, II, III, IV, and V), based on the overall gene expression patterns during pupal development. We screened 99 specific time-dependent expressed genes (stage-specific DEGs) to determine the different intrapuparial stages based on comparison of the gene expression levels during the 11 developmental intrapuparial stages of A. grahami. We observed that 55 DEGs showed persistent upregulation during the development of intrapuparial A. grahami. We then selected four DEGs (act79b, act88f, up and ninac) which presented consistent upregulation using RT-qPCR (quantitative real-time PCR) analysis, along with consideration of the maximum fold changes during the pupal development. We conducted nonlinear regression analysis to simulate the calculations of the relationships between the expression levels of the four selected DEGs and the developmental time of intrapuparial A. grahami and constructed fitting curves. The curves demonstrated that act79b and ninac showed continuous relatively increasing levels. Conclusions: This study redefined the intrapuparial stages of A. grahami based on expression profiles of developmental transcriptomes for the first time. The stage-specific DEGs and those with consistent tendencies of expression were found to have potential in age estimation of intrapuparial A. grahami and could be supplementary to a more accurate prediction of PMI.

Abstract Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.

Objective: The aim was to test the Belgrade age formula based on the calculation of open apices of two permanent mandibular teeth on a Bosnian children population and compare its accuracy with European formula. Material and methods: We included 412 panoramic images of children (204 female and 208 male) 7 to 13 years of age. We assessed the performance of both methods (the European formula and the BAF) and compared their results in both sexes. Results: The results showed a high point of average understanding between the age estimated by chronological age and the European formula (ICC=0.927, 95% CI 0.904–0.944, p

This study introduces the concept of relative age position learning to supplement the learning process for age estimation models. Drawing inspiration from feature recalibration modules that prioritize features based on their importance, a novel age-based reweighting module is developed to enhance feature representation in the proposed age estimation method. The proposed reweighting module obtains the features of the selected references for each age and further exploits them to reweight the features of the input images based on age importance. The resulting recalibrated features are then assessed for relative age position prediction. In addition, to achieve better generalization performance in age estimation, a gender prediction head is added to create a multi-task learning network that simultaneously predicts the ages and genders of the input images. Through extensive experiments, we demonstrate that the proposed approach outperforms other state-of-the-art age estimation methods on three challenging benchmark datasets for facial age estimation: AgeDB, AFAD, and CACD.

Background: Age estimation plays a crucial role in identifying an unknown individual in the event of any mass disaster or crime. One of the important noninvasive procedures that may have an impact on determining an individual's age is exfoliative cytology. Aim and Objective: Exfoliated buccal mucosa cells were used in the study to determine the participant's age by assessing the changes in nuclear diameter (ND), nuclear–cytoplasmic (N: C) ratio, and cell diameter (CD). Materials and Methods: With the help of a moistened wooden spatula and a light scraping motion, buccal smears were taken from 100 healthy and normal individuals across various age groups. The samples were then preserved with 95% ethanol and stained using ultrafast Papanicolaou stain. Cell and nuclear diameter were measured for each of these cells using the ImageJ software, and N: C was calculated from the two parameters. The N: C was calculated from CD and ND for each subject. One-way ANOVA tests were used for statistical analysis. Results: A significant difference in the diameter of cell and nucleus with the individual's age (P < 0.05) was noticed. The N: C ratio reduces with increasing age but is not consistent. Conclusion: Buccal smears show age-related alterations that could be utilized as an effective tool for age estimation.

Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.