Estimation Of Chronological Age Research Articles

Chronological age estimation based on dental mineralization for Syrian population.

Dental age assessment based on evaluating dental mineralization status is one of the most common methods used in forensic practice. The aim of this study is to enhance the accuracy of age diagnostics and provide reference data from the Syrian population for forensic application. After several selection steps, a total of 280 orthopantomograms (OPGs) from 140 males and 140 females from the Syrian population divided into 14 age groups between 12 and 25 years were analysed. Based on Demirjian's classification system, the mineralization stages of third molars (18, 28, 38 and 48) as well as lower second molars (37 and 47) were evaluated. Statistical investigations and evaluations were carried out to estimate the marginal probabilities of the subjects having attained ages 14 and 18 by generalized estimating equation models. Our results show that no significant differences can be revealed in the mineralization status with respect to jaw side and sex. In the Syrian population, third molars showing mineralization stage G provide evidence of reaching the age of 14 years with the highest standard of proof ("beyond reasonable doubt"). A completed mineralization in lower second molars (stage H) provides very high marginal probabilities (more than 90%) of the subjects having attained age 14 years. Nevertheless, this cannot exclude an age under 14 years. For the age threshold of 18 years, third molars showing incomplete root development (G dental stage or lower) are associated with a low probability (less than 40%) of the subject having reached 18 years of age. A person's probability of having attained 18 years of age is very high (82- 95%) when the roots of third molars are fully developed (stage H). Nevertheless, third molars at stage H do not conclusively exclude an age under 18 years.

Profile of Medico-legal Age Estimation Cases in a Tertiary Hospital in Kathmandu

Introduction: Medico-legal age estimation is the estimation of chronological age of an individual which is based upon analysis of physical development of the body, development of secondary sexual traits, tooth eruption, appearance of ossification centers of bones, and the degree of epiphyseal union. Chronological age of the subject whether a minor (less than 18 years) or an adult determines the fate of the case in the court of law. Objective: The present study revealed varied profiles of the perpetrator and the victim who came for medico-legal age estimation. Methods: This is a retrospective, descriptive cross-sectional study conducted in the Department of Forensic Medicine, Maharajgunj Medical Campus, Institute of Medicine (IoM). The data of medico-legal age estimation of 5 years period was analyzed using the statistical software SPSS 20. Results: Total of 313 individuals were studied. Majority of the individuals were within the age group of 16-17 years. Males (70.3 %) outnumbered females (29.7 %). Theft (31.9 %) was common among subjects, followed by sexual assault (18.2 %) and majority of subjects (59.7%) came from Province 3. Sixty percent of the subject’s alleged age, matched the estimated ages. Conclusion: This study shows the status of children in crime which demands attention of concerned authority. Males were significantly involved in theft and females were victims of sexual assault. Forty percent of the alleged age given by the subject differed from the estimated age, to change the fate of the case. These results could greatly benefit the judiciary system of Nepal.

A Comparison of Two Methods of Dental Age Estimation in a Population of Saudi Children and Adolescents.

This study aimed to compare and evaluate the accuracy of the Demirjian (DE) and the London Atlas (LAE) dental age estimation methods in a Saudi population sample. This retrospective cross-sectional study used digital radiographs from electronic health records in three different dental institutes. In total, 357 male and 354 female (ages 5-15 years) digital orthopantomograms were selected for age estimation. The mean difference between the chronological age (CA) and age estimation method among males and females was 0.03 ± 0.34 and 0.00 ± 0.34, respectively, for LAE and 0.55 ± 0.84 and 0.76 ± 0.51, respectively, for DE. The mean difference between the LAE and DE methods among males and females was 0.52 ± 0.89 and -0.76 ± 0.57, respectively. No statistically significant difference between CA and LAE was found in either males (p = 0.079) or females (p = 0.872). A statistically significant difference was found between CA and DE in both genders (p < 0.001). A statistically significant difference was found between the LAE and DE groups (p < 0.001) in both genders. An overestimation of dental age was observed with DE compared with that in CA. LAE showed higher accuracy than CA, with no clinically significant difference. Although the difference between the LAE and DE methods was insignificant, the LAE method proved to be more accurate.

Trabecular bone microstructure parameters as predictors for chronological age: a systematic review.

Estimating chronological age is crucial in forensic identification. The increased application of medical imaging in age analysis has facilitated the development of new quantitative methods for the macroscopic evaluation of bones. This study aimed to determine the association of age-related changes in the trabecular microstructure with chronological age for age estimation in forensic science through different non-invasive imaging techniques. This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. An electronic search was performed with PubMed/MEDLINE, Scopus, and Cochrane databases as well as with a Google Scholar search. Qualitative synthesis was performed using the Anatomical Quality Assessment tool. A detailed literature search yielded 3467 articles. A total of 14 articles were ultimately included in the study. A narrative approach was employed to synthesize the data. Microcomputed tomography, high-resolution peripheral quantitative computed tomography, and cone beam computed tomography have been used for the quantitative estimation of age. These imaging techniques aid in identifying the trabecular bone microarchitectural parameters for chronological age estimation. Age-related changes in trabecular bone included a decrease in the bone volume fraction, trabecular number, and connectivity density and an increase in trabecular separation. This study also revealed that morphometric indices vary with age and anatomical site. This study is registered with the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CDRD42023391873.

Demirjian's and Cameriere's Methods for the Assessment of Dental Age Estimation in Children from a Southern Brazilian City.

The chronological age estimation of living individuals is a crucial part of forensic practice and clinical practice, such as in orthodontic treatment. It is well-known that methods for age estimation in living children should be tested on different populations. Ethnic affiliations in Brazil are divided into several major groups depending on the region, with the south of Brazil being known for its German immigration. (1) Background: This study aimed to evaluate the correlation between chronological age and dental age using Demirjian's method and Cameriere's method in a group of children from Joinville, South Brazil to investigate if both methods can be used to estimate dental age in this population. (2) Methods: The sample consisted of 229 panoramic radiographs (119 were males and were 110 females) from Brazilian children (ages ranging from 6 to 12 years). The chronological age at the time of the panoramic radiographic exam was calculated for each child. The dental age was estimated according to Demirjian's method and Cameriere's method. All continuous data were tested for normality by using the Shapiro-Wilk test. The Pearson correlation coefficient test was applied. An alpha of 5% (p < 0.05) was used for all analyses. (3) Results: The mean chronological age was 8.75 years. According to Demirjian's method, the mean dental age was 9.3 years, while according to Cameriere's method, the mean dental age was 8.66 years. A strong correlation between chronological age and dental age according to Demirjian (r = 0.776 and p < 0.0001) and Cameriere (r = 0.735 and p < 0.0001) was observed for both genders. (4) Conclusions: Both methods presented a good correlation with chronological age in the studied population and could be used to assess dental age in this population.

Accuracy of the Mincer Method for Dental Age Estimation in a Honduran Population

Abstract Aim: This study aims to determine the accuracy of the Mincer method for estimating dental age (DA) using digital panoramic radiographs (PR) in a Honduran population aged 14–22 years. Materials and Methods: This observational, cross-sectional, retrospective study was applied in a sample of 134 digital PR from a private dental center in Comayagua (72 females and 62 males; mean age 18 ± 3 years). A calibrated observer (kappa ≥0.83) analyzed DA with the Demirjian and Mincer method in all third molars (3M) from stage D (complete crown) to H (apex closure) differentiating by sex. The decimal DA of each individual was calculated from the average of the four 3M. Estimation of chronological age (CA) according to the Mincer method was obtained with linear regression formulas. Results: The results revealed that the mean and standard error of the difference between CA and DA was 0.58 ± 0.13 years (male 0.5 ± 0.19 and female 0.65 ± 0.19). Age groups showed higher overestimation (&gt;1.1) from 14 to 17 years (50%–100%), agreement (±1.1 years) at 18 years (66.7%–70%), and underestimation (&lt;–1.1) from 21 and 22 years (42.9%–100%; P = 0.000). The prediction equations of CA according to DA were statistically significant (P = 0.000) explaining 66.5% of the total sample (error = 1.465), 84.5% in males (error = 1.367), and 79.3% in females (error = 1.548). Conclusion: This study concludes that the Mincer method obtained a reasonable prediction to estimate the age of the majority with improvement in the adjustments with the age estimation formula in a specific population in Comayagua, Honduras.

Estimation of chronological age of Risso’s dolphin (Grampus griseus) based on DNA methylation frequency

Age of cetaceans is an important data set for their conservation and management. Although the chronological age of a toothed whale has traditionally estimated from the number of annual tooth growth layer groups (GLGs), this method has potential difficulties such as errors due to the subjectivity of the age-readers and time-consuming sample processing, in addition to lethal tooth sampling in many cases. To overcome these difficulties through a non-lethal approach, considerable amount of efforts has been made in recent years to construct chronological age estimation models based on the correlation between chronological and biological age expressed as the frequency of DNA methylation at CpG (cytosine-phosphate-guanine) sites. The present study, by using next-generation sequencing on DNA extracted from the skin tissues of 30 individuals (15 males and 15 females), performed methylation analysis on multiple CpG sites in the three genes previously reported as age-linked in cetaceans (GRIA2, TET2, and CDKN2A) to construct an age estimation model for Risso’s dolphins. Key aspects of this study are: (1) the chronological ages of Risso’s dolphins of wild origins used in this study had been determined by the GLGs of teeth prior to this study, and (2) as Risso’s dolphins are said to have a lifespan of 30 years, samples were collected from individuals aged 1–29 years, covering from young to old ages. Among the 11 CpG sites in GRIA2, 6 CpG sites in TET2, and 7 CpG sites in CDKN2A analyzed, the CpG sites in GRIA2 showed a statistically significant correlation with age, whereas those in TET2 and CDKN2A did not show such a strong correlation. Using multiple regression analysis on the CpG sites in the three genes, the best fitted model was expressed as follows: y=-0.7548 + 5.6164x(GRIA2_CpG3)+2.6187x(TET2_CpG6)-1.5907x(CDKN2A_CpG3) (R²=0.7062, residual mean=2.98, standard deviation=2.3). With the mean error of 2.98 years between the estimated and known chronological ages, the accuracy of this model was found to be comparable to or higher than that of previously reported age estimation models for cetaceans. This study demonstrated for the future that the age of wild Risso’s dolphins can be estimated by using their skin samples collected with minimal invasiveness.

Evaluation of DNAmAge in paired fresh, frozen, and formalin-fixed paraffin-embedded heart tissues.

The continued development in methylome analysis has enabled a more precise assessment of DNA methylation, but treatment of target tissue prior to analysis may affect DNA analysis. Prediction of age based on methylation levels in the genome (DNAmAge) has gained much interest in disease predisposition (biological age estimation), but also in chronological donor age estimation in crime case samples. Various epigenetic clocks were designed to predict the age. However, it remains unknown how the storage of the tissues affects the DNAmAge estimation. In this study, we investigated the storage method impact of DNAmAge by the comparing the DNAmAge of the two commonly used storage methods, freezing and formalin-fixation and paraffin-embedding (FFPE) to DNAmAge of fresh tissue. This was carried out by comparing paired heart tissue samples of fresh tissue, samples stored by freezing and FFPE to chronological age and whole blood samples from the same individuals. Illumina EPIC beadchip array was used for methylation analysis and the DNAmAge was evaluated with the following epigenetic clocks: Horvath, Hannum, Levine, Horvath skin+blood clock (Horvath2), PedBE, Wu, BLUP, EN, and TL. We observed differences in DNAmAge among the storage conditions. FFPE samples showed a lower DNAmAge compared to that of frozen and fresh samples. Additionally, the DNAmAge of the heart tissue was lower than that of the whole blood and the chronological age. This highlights caution when evaluating DNAmAge for FFPE samples as the results were underestimated compared with fresh and frozen tissue samples. Furthermore, the study also emphasizes the need for a DNAmAge model based on heart tissue samples for an accurate age estimation.

Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Dental age estimation: A comparative study of convolutional neural network and Demirjian's method

The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4–17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.

Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models

Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model’s performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Estimation of Human Chronological Age from Buccal Swab Samples through a DNA Methylation Analysis Approach of a Five-Locus Multiple Regression Model.

Recent advancements in forensic genetics have facilitated the extraction of additional characteristics from unidentified samples. This study delves into the predictive potential of a five-gene (ELOVL2, FHL2, KLF14, C1orf132, and TRIM59) methylation rate analysis for human age estimation using buccal swabs collected from 60 Italian volunteers. The methylation levels of specific CpG sites in the five genes were analyzed through bisulfite conversion, single-base extension, and capillary electrophoresis. A multivariate linear regression model was crafted on the training set, then the test set was employed to validate the predictive model. The multivariate predictive model revealed a mean absolute deviation of 3.49 years in the test set of our sample. While limitations include a modest sample size, the study provides valuable insights into the potential of buccal swab-based age prediction, aiding in criminal investigations where accurate age determination is crucial. Our results also highlight that it is necessary to investigate the effectiveness of predictive models specific to biological tissues and individual populations, since models already proven effective for other populations or different tissues did not show the same effectiveness in our study.

Telomere length is not a useful tool for chronological age estimation in animals.

Telomeres are short repetitive DNA sequences capping the ends of chromosomes. Telomere shortening occurs during cell division and may be accelerated by oxidative damage or ameliorated by telomere maintenance mechanisms. Consequently, telomere length changes with age, which was recently confirmed in a large meta-analysis across vertebrates. However, based on the correlation between telomere length and age, it was concluded that telomere length can be used as a tool for chronological age estimation in animals. Correlation should not be confused with predictability, and the current data and studies suggest that telomeres cannot be used to reliably predict individual chronological age. There are biological reasons for why there is large individual variation in telomere dynamics, which is mainly due to high susceptibility to a wide range of environmental, but also genetic factors, rendering telomeres unfeasible as a tool for age estimation. The use of telomeres for chronological age estimation is largely a misguided effort, but its occasional reappearance in the literature raises concerns that it will mislead resources in wildlife conservation.

Novel Framework for Brain Age Prediction using Graph Neural Networks

AbstractBackgroundAging is among the largest risk factors for neurodegeneration which can be accelerated by age‐related pathologies. Hence, individuals may age at different rates reflected by a gap in biological estimates of age and actual chronological age. Graph neural networks (GNN) are deep learning models that leverage spatial information inherent in the dataset for inference. Here, we develop an MRI cortical thickness (CT) based brain age prediction framework using GNNs and illustrate that it provides a feasible mechanism to identify contributing regions leading to elevated brain age gap in Alzheimer’s disease (AD).MethodWe used ANTs to calculate CT in 100 parcels from 3T MRI of 170 healthy controls (HC; age = 64.26±8.27y, 101 females), 53 mild cognitive impairment (MCI; age = 68.56±8.58y, 22 females), and 62 AD (age = 67.25±8.83y, 30 females) individuals. A GNN model based on the anatomical covariance matrix from HC cohort was trained to predict chronological age from associated CT for HC cohort. GNN output was evaluated as an unweighted mean of entities associated with the brain regions in the final layer. Hence, individual contributions to the GNN output by each brain region could be quantified. Final brain age prediction was obtained after removing age‐bias from GNN outputs using linear regression model. For all subjects, we evaluated residuals at regional level (regional age‐gap) by comparing the contribution of each parcel with corresponding GNN output. The residual between predicted brain age and chronological age (age‐gap) was also evaluated. See Figure 1 for an overview.ResultANCOVA (with correction for age and gender) with post‐hoc analyses revealed that regional age‐gap was greater for AD relative to HC in inferior parietal, temporal pole, inferior temporal, medial temporal, and superior frontal regions (Bonferroni corrected p‐value&lt;0.05). Also, regional age‐gap was greater for MCI relative to HC for inferior parietal and temporal pole regions (Figure 2). Subsequently, age‐gap was elevated in AD (mean = 5.64y) and MCI (mean = 2.66y) relative to HC (mean = 0y) reflecting a linear increase in age‐gap with disease stage (Figure 3).ConclusionOur novel framework identified regional CT associations with age‐gap in AD and MCI, suggesting that this GNN approach may be used to further our understanding between aging and neurodegeneration.

A 5‐hydroxymethylation‐based clock derived from cell‐free DNA in blood reveals accelerated epigenetic aging in patients with Alzheimer’s disease

AbstractBackgroundEnvironmental exposure and lifestyle factors can induce epigenetic aging acceleration that has been implicated in various complex diseases. Enabling technologies compatible with convenient biospecimens, e.g., 2‐3 mL of blood, will facilitate clinical application. Targeting 5‐hydroxymethylcytosines (5hmC), a stable and under‐investigated regulator in the human genome, may improve our understanding of epigenetic aging and its implications in Alzheimer’s disease (AD).MethodUsing our previously published genome‐wide 5hmC profiles derived from cell‐free DNA (cfDNA) in blood (∼1,200 healthy individuals [HEA], 48.2±10.9 yrs, 59.3% males, 2:1 randomization for training and testing) using the 5hmC‐Seal technique and next‐generation sequencing, a 5hmC‐based clock was constructed to characterize epigenetic ages (5hmC‐age). Epigenetic‐chronological age correlation, median error, and mean aging acceleration were evaluated. An independent set of cfDNA samples collected from 20 subjects (86.2±4.3 yrs, 60.0% males) with pathologically confirmed AD and 17 matched controls on chronological age, sex, and education (86.8±3.8 yrs, 58.8% males) without AD (non‐AD), from the Rush Memory and Aging Project, were employed to calibrate and examine epigenetic aging in AD.ResultChronological ages were found to be associated with the 5hmC modification levels of a significant proportion of genes in HEA, particularly those genes involved in immune response, inflammation, metabolism, and environmental exposure. An epigenetic clock comprised of ∼300 genes was then constructed. In HEA samples, we observed Pearson correlations of 0.88 and 0.65, median errors of 3.7 and 4.9 years, and mean aging accelerations of 0.03 and 0.57 years between chronological age and 5hmC‐age in training and testing samples, respectively, indicating that 5hmC‐age was a robust estimation of chronological age. In the independent small sample of AD and non‐AD subjects, a general acceleration of 5hmC‐ages relative to chronological ages was observed, with a calibrated mean aging acceleration of 2.55 years in persons with AD relative to non‐AD subjects.ConclusionThe 5hmC‐Seal appears to provide a convenient tool to evaluate epigenetic aging in blood that may facilitate preventive and precision medicine. Specifically, accelerated epigenetic aging was found in AD, suggesting that epigenetic age assessed in blood may be a novel biomarker for this neurodegenerative disease. This study was supported partially by the NIH (RF1AG074549, R01AG17917).

PREDICTIVE ABILITY OF MANDIBULAR THIRD MOLAR IN ESTIMATION OF CHRONOLOGICAL AGE

Objective: To determine the predictive ability of mandibular third molar in estimation of chronological age&#x0D; Material and methods: This is cross sectional descriptive study was conducted at Saidu college of Dentistry, Swat on 120 participants. The inclusion criteria were both males and females, age from 13 to 25 years, having good quality panoramic X-ray clearly showing lower third molar area. Participants with dental anomalies like oligodontia, Dentinogenesis/Amelogenesis imperfecta, fracture affecting angular region of mandible were excluded. Age, sex and tooth mineralization of lower third molar were recorded. Linear regression was applied using age as dependent and maturation stages of third molar as independent variable.&#x0D; Results: The mean age was 16.85±2.39 years. The females were 78 (65%) and males were 42 (35%). Overall the model shows that in 79.9% participant the variation in age can be explained by maturation stages of third molar and gender. The regression equation for age estimation is age= 11.42+1.68x(maturation stage) + 0.13x(gender).&#x0D; Conclusion: Mandibular third tooth maturation stages are useful in prediction of chronological age of the patients.

The accuracy of Cameriere methods in Turkish children: chronological age estimation using developing teeth and carpals and epiphyses of the ulna and radius.

The aim of the present study was to develop a specific formula by measuring the developing teeth, carpal bones, and epiphyses of the ulna and radius to determine the chronological age in Turkish children. The left developing permanent mandibular teeth were evaluated, and the number of teeth with closed apex was recorded. The distance between the inner sides of open apex/apices was measured by using the ImageJ program and divided by the tooth length. The sum of the normalized open apices was also calculated. The carpal area (Ca), covering the epiphyses of ulna and radius and the carpal bones, was measured on the X-rays of left hand. The areas of each carpal bone and epiphyses of the ulna and radius were measured, and these measurements were added together to obtain the bone area (Bo). The Bo/Ca ratio between the total area of carpal bones and the carpal area was calculated to normalize the measurements. The accuracy of the equations formulated by Cameriere was evaluated, and a new regression equation was developed accordingly. The new formula showed no statistically significant difference between the chronological and the estimated age for females, males, and total sample. The new formula, which hit the age with 72.80% accuracy, was more successful in predicting chronological age than other adjusted regression equations. The new regression model, created for the Turkish children by using both developing teeth and hand-wrist bones, was considerably successful in estimating the chronological age.

Relationship between Pulp–Tooth Area Ratio and Chronological Age among Saudi Arabian Adults: A Cone Beam Computed Tomography Image Analysis

The pulp–tooth area ratio (PTR) from radiographic dental archives is considered useful for age estimation in the forensic field. Since there have been no studies conducted in the Saudi Arabian population using the PTR method, this study aimed to assess the relationship between the pulp–tooth area ratio of maxillary canines and central incisors and chronological age among Saudi Arabian adults using CBCT and to compare the selected teeth’s predictive power. For this, 100 CBCT scans of 62 male and 38 female patients aged between 20 and 60 years were assessed using OnDemand 3D DentalTM software (Cybermed Co., Tustin, CA, USA) in the axial and sagittal section of each tooth. The mean age estimated using the PTR of the maxillary incisors (39.98 years) was higher than the canines’ (37.9 years). A negative correlation coefficient ‘r’ value was seen between PTR and age. The strongest inverse correlation was noted between age and incisor axial view (0.47) (p &lt; 0.05), Maxillary central incisors had higher R2 in both genders (males: PTRS-0.18, PTRA-0.17; females: PTRS-0.19, PTRA-0.35) than canines. We conclude a weak correlation between the pulp–tooth ratio and chronological age estimation. CBCT pulp–tooth ratios of axial and sagittal sections of incisors were inconclusive in estimating the age of Saudi Arabian individuals owing to their low coefficient of estimation. Among the tooth types studied, maxillary incisors were better predictors than canines.

Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives.

Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.