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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. [ABSTRACT FROM AUTHOR]

Objectives: Age at death estimation is a key element to many research questions in biological anthropology, archeology, and forensic science. Dental cementum is a tissue of choice for the estimation of age at death in adult individuals as it continues deposition for the entirety of an individual's life. Previous works have devised regression formulas correlating cementum thickness to age at death. However, interpopulation variances are unknown, and it is therefore not clear whether regressions based on a single population are applicable to individuals with different ancestries. Materials and Methods: Here, we use a sample (n = 52) of teeth from individuals with known age at tooth extraction/death of European, African, and East Asian ancestry to assess whether there are interpopulations differences in cementum growth rate. We measured growth rate in four different areas (2nd and 5th decile of both the lingual and buccal aspect of the root) of each tooth and used nonparametric tests to evaluate population differences in growth rate between homologous regions of the teeth. Results: The results of the analyses show that, even after controlling for tooth size, individuals of European ancestry have significantly lower growth rates than those of both African and East Asian ancestry across all four tooth areas. Discussion: These results call into question the applicability of the regression formulas derived from European ancestry individuals to individuals of other ancestries. [ABSTRACT FROM AUTHOR]

Introduction: Evaluation of the eruption of mandibular third molars in orthopantomograms (OPGs) is a method of forensic age assessment. The objective of our study was to provide valid reference data for this trait within a population of black South Africans. The study was guided by the criteria for reference studies in age assessment. Materials and methods: A study population from Pretoria, South Africa comprising 670 OPGs obtained from 338 black females and 332 black males aged between 15.00 and 25.97 years was analysed. All OPGs were performed for medical indication during the period from 2011 to 2022 and were retrospectively evaluated. From the 670 OPGs, a total of 1021 mandibular third molars were evaluated. The assessment of mandibular third molars was conducted using the staging scale presented by Olze et al. in 2012. Two experienced dentists evaluated the OPGs independently of each other. If the two examiners diverged in their assessments, a consensus stage was assigned. Results: As expected, the mean, median and minimal age increased with higher stages for both teeth and both sexes. The minimum age recorded for stage D, indicating complete tooth eruption, was 15.79 years in females and 16.62 years in males. Conclusion: As it is the case for previous reference studies in other countries, placing exclusive reliance on the evaluation of mandibular third molar eruption is inadequate for ascertaining the age of majority among Black South Africans. Future studies need to examine if our results are transferable to other countries in Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Skeletal age markers are often used to estimate age‐at‐death to reconstruct population survivorship in the past, which is essential to draw conclusions about population health, fertility, and mortality. Transition Analysis age estimation was developed to address common issues facing traditional age estimation methods such as age mimicry and broad ranges for middle and older aged adults. However, some studies have shown Transition Analysis' limitations in overcoming these challenges, with regards to the method's accuracy and bias when applied at the individual‐level. It was previously found that the method performed poorly in a sample from the Hamann‐Todd Osteological Collection due to a series of factors. This study re‐examines the application of Transition Analysis (ADBOU) age estimation in a sample from the Hamann‐Todd Collection for reconstructing the survivorship profile of this skeletal sample. Although Transition Analysis is argued to be better suited for reconstructing trends in population mortality compared to traditional age estimation methods, results of Kaplan–Meier and log rank tests show that Transition Analysis produced significantly different survival curves compared to the known survival curves for White males (χ2 = 6.828, p = 0.009) and females (χ2 = 11.270, p = 0.001), but not for Black males (χ2 = 1.363, p = 0.243) and females (χ2 = 0.071, p = 0.790). Age‐at‐death distributions between the estimated maximum likelihoods and known ages also differed significantly (t = 4.249, p < 0.001). Therefore, caution should be employed when drawing conclusions from trends in survivorship based on skeletal age estimates, even when using more recently developed methods such as Transition Analysis, as the informative priors in ADBOU may not be accurate for all populations. [ABSTRACT FROM AUTHOR]

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in using DNA methylation analysis for age estimation. Despite this growing interest, there is a scarcity of research on the potential of DNA methylation as a biomarker for age estimation in Indonesia. This study aims to investigate the applicability of ELOVL2, PRKG2, and EDARADD genes for forensic identification in the 11–20 age group among Indonesians. This research utilizes 43 archived blood samples from healthy individuals who underwent blood tests at the Gatot Soebroto Army Hospital (RSPAD) in Central Jakarta, Indonesia. The methylation-specific PCR (MSP) technique assessed the DNA methylation level. The key findings of this study include (1) a strong positive correlation between methylation levels in the ELOVL2 gene and age; (2) a strong negative correlation between methylation levels in PRKG2 and EDARADD genes with age; (3) the development of three linear regression formulas for age prediction; and (4) mean absolute error (MAE) values derived from this research, which are ±0.48 for ELOVL2 gene regression formula, ±0.58 for PRKG2 gene regression formula, and ±0.72 for EDARADD gene regression formula. In summary, this study explores the potential of DNA methylation analysis for age estimation in Indonesia, focusing on ELOVL2, PRKG2, and EDARADD genes in the 11–20 age group. The findings underscore the applicability of DNA methylation analysis in forensic identification and age estimation, paving the way for future research in this field. [ABSTRACT FROM AUTHOR]

Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning technology. Notably, computer vision solutions have significantly contributed to these achievements. Human face analysis, a core area of computer vision, has gained considerable attention due to its wide applicability in fields such as law enforcement, social media, and marketing. However, existing methods for facial age estimation often struggle with accuracy due to limited feature extraction capabilities and inefficiencies in learning hierarchical representations. This paper introduces a novel framework to address these issues by proposing a Multi-Stage Deep Neural Network (MSDNN) architecture. The MSDNN architecture divides each CNN backbone into multiple stages, enabling more comprehensive feature extraction, thereby improving the accuracy of age predictions from facial images. Our framework demonstrates a significant performance improvement over traditional solutions, with its effectiveness validated through comparisons with the EfficientNet and MobileNetV3 architectures. The proposed MSDNN architecture achieves a notable decrease in Mean Absolute Error (MAE) across three widely used public datasets (MORPH2, CACD, and AFAD) while maintaining a virtually identical parameter count compared to the initial backbone architectures. These results underscore the effectiveness and feasibility of our methodology in advancing the field of age estimation, showcasing it as a robust solution for enhancing the accuracy of age prediction algorithms. [ABSTRACT FROM AUTHOR]

At the time of death, estimating the age of unknown human remains is valuable for post-mortem identification. However, no sufficiently reliable methods have been developed to date. This study aimed to investigate the relationship between aortic measurements and age of human remains and to assess the feasibility of age estimation using aortic measurements. Data were collected from 550 Japanese dead bodies (321 male and 229 female subjects), ranging in age from infancy to older adulthood (age 0–97 years). During autopsy, aortic thoracic circumference (ATC) and aortic abdominal circumference (AAC) were measured using a ruler, and the sum of ATC and AAC (STA) was calculated. A simple linear regression analysis using the least-squares method was performed to evaluate the correlation between aortic measurements and age in years and to establish the formula for age estimation using aortic measurements. Additionally, four-parameter logistic curves were created for age estimation. Statistically significant positive correlations were found between age and each aortic measurement. Standard errors of the estimation values using ATC and STA were 9.90–12.27 and 8.88–12.28 years, respectively. The results suggest that age estimation based on aortic measurements is useful for forensic investigation. [ABSTRACT FROM AUTHOR]

Bias in facial recognition systems often results in unequal performance across demographic groups. This study addresses this by investigating how dataset composition affects the performance and bias of age estimation models across ethnicities. We fine-tuned pre-trained Convolutional Neural Networks (CNNs) like VGG19 on the diverse UTKFace dataset (23,705 samples: 10,078 White, 4526 Black, 3434 Asian) and APPA-REAL (7691 samples: 6686 White, 231 Black, 674 Asian). Our approach involved adjusting dataset compositions by oversampling minority groups or reducing samples from overrepresented groups to mitigate bias. We conducted experiments to identify the optimal dataset composition that minimizes performance disparities among ethnic groups. The primary performance metric was Mean Absolute Error (MAE), measuring the average magnitude of prediction errors. We also analyzed the standard deviation of MAE across ethnic groups to assess performance consistency and equity. Our findings reveal that simple oversampling of minority groups does not ensure equitable performance. Instead, systematic adjustments, including reducing samples from overrepresented groups, led to more balanced performance and lower MAE standard deviations across ethnicities. These insights highlight the importance of tailored dataset adjustments and suggest exploring advanced data processing methods and algorithmic tweaks to enhance fairness and accuracy in facial recognition technologies. [ABSTRACT FROM AUTHOR]

A computer-based age estimation is a technique that predicts an individual's age based on visual traits derived by analyzing a 2D picture of the individual's face. Age estimation is critical for access control, e-government, and effective human–computer interaction. The other-race effect has the potential to cause techniques designed for white faces to underperform when used in a region with black faces. The outcome is the consequence of intermittent training with faces of the same race and the encoding structure of the trained face images, which is based on the feature extraction technique used. This study contributes to a constructive comparison of three feature-extraction techniques, namely, local binary pattern (LBP), Gabor Wavelet (GW), and wavelet transformation, used in the development of a genetic algorithm (GA)-artificial neural network (ANN)-based age estimation system. The feature extraction techniques used are proven to produce a wealth of shape and textural information. The GA-ANN constitutes the age classifier module. The correct classification rate was chosen as the performance metrics in this study. The results demonstrated that the LBP is a more robust representation of the black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and Wavelet transformation age estimation systems, respectively. [ABSTRACT FROM AUTHOR]

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Along with the recent acknowledgement of the World Anti‐Doping Agency to use dried blood spot (DBS) samples for routine doping control purposes, there have been propositions to use DBS as a matrix that allows regular proactive remotely supervised self‐sampling, providing potential longitudinal monitoring of an athlete's exposure to doping agents. However, several organizational aspects have to be considered before implementation, such as the verification of the sample collections time point. Based on a previous untargeted proteomics workflow utilizing liquid chromatography–high‐resolution mass spectrometry (LC–HRMS) to identify protein/peptide markers to define the time since deposition of a bloodstain, the aim of the current study was to develop a targeted LC–HRMS/MS analytical method for promising peptidic target analytes. A long‐term DBS storage experiment was carried out over a 3‐month period (sample collection time points: 0, 2, 4, 7, 14, 21, 28, 42, 56, 70, 84 and 91 days) with DBS samples of 10 volunteers for longitudinal investigation of signal abundance changes of targeted peptide sequences at different storage temperatures (room temperature [RT], 4°C and −20°C). Prior to experimental analysis, LC–HRMS/MS method characteristics were successfully assessed, including intraday precision, carryover and sample extract stability. For estimation of DBS sample collection time points, ratios of two peptides that originate from the same protein prior to tryptic digestion were created. Two targeted peptide area ratios were found to significantly increase after being stored at RT for 28 days, representing potential markers for future use in routine doping controls that contribute to advancing complementary avenues in anti‐doping. [ABSTRACT FROM AUTHOR]

Understanding the demography of wildlife populations is a key component for ecological research, and where necessary, supporting the conservation and management of long‐lived animals. However, many animals lack phenological changes with which to determine individual age; therefore, gathering this fundamental information presents difficulties. More so for species that are rare, highly mobile, migratory and those that reside in inaccessible habitats. Until recently, the primary method to measure demography is through labour intensive mark‐recapture approaches, necessitating decades of effort for long‐lived species. Gadfly petrels (genus: Pterodroma) are one such taxa that are overrepresented with threatened and declining species, and for which numerous aspects of their ecology present challenges for research, monitoring and recovery efforts. To overcome some of these challenges, we developed the first DNA methylation (DNAm) demography technique to estimate the age of petrels, using the epigenetic clock of Gould's petrels (Pterodroma leucoptera). We collected reference blood samples from known‐aged Gould's petrels at a long‐term monitored population on Cabbage Tree Island, Australia. Epigenetic ages were successfully estimated for 121 individuals ranging in age from zero (fledgling) to 30 years of age, showing a mean error of 2.24 ± 0.17 years between the estimated and real age across the population. This is the first development of an epigenetic clock using multiplex PCR sequencing in a bird. This method enables demography to be measured with relative accuracy in a single sampling trip. This technique can provide information for emerging demographic risks that can mask declines in long‐lived seabird populations and be applied to other Pterodroma populations. [ABSTRACT FROM AUTHOR]

Background: Forensic anthropological examinations help in identification of unidentified human remains. This study aims to establish population-specific standards for age estimation from the sternal ends of left fourth ribs in the Nepalese population. A quantitative, observational study was conducted on 387 Nepalese deceased (106 females, 281 males) between November 2021 and March 2023. Left fourth ribs were collected, macerated and examined for the study. The variables examined included pit depth, pit shape, rim and wall configurations and rib phase. Results: Pit depth significantly predicted age, β = 0.642, t(385) = 16.42, p < 0.001. Pit depth also explained a significant proportion of variance in age, R2 = 0.41, F(1, 385) = 269.54, p < 0.001. The age estimates from pit depth stage were 7–36 (stage 0), 7–49 (stage 1), 16–51 (stage 2), 10–58 (stage 3), 22–70 (stage 4) and 32–81 (stage 5). Age estimates for pit shape were 17–20 (stage 0), 13–48 (stage 2), 15–55 (stage 3), 24–68 (stage 4) and 39–82 (stage 5). Age estimates for rim and wall configurations were 17–20 (stage 0), 13–47 (stage 2), 17–53 (stage 3), 24–68 (stage 4) and 40–82 (stage 5). Similarly, age estimates for rib phase were 17–20 (phase 0), 18–20 (phase 1), 13–46 (phase 2), 18–50 (phase 3), 23–60 (phase 4), 32–71 (phase 5), 50–76 (phase 6), 44–81 (phase 7) and 62–85 (phase 8). The study also examined the use of transitional analysis to develop posterior probability distributions for estimation of age using rib phases. Conclusion: This study found significant differences in the age estimates from previous studies and shows the importance of developing population-specific models for use in forensic anthropology. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the utility of the proximal femur in the forensic age estimation by assessing changes in bone densities through radiographs. Using Otsu's threshold, bone density was quantified by counting all white pixel values within selected regions of interest, which include femoral head (FH), femoral neck (FN), Ward's triangle (WT), and greater trochanter (GT) from 354 left femora of Northern Thai descent. The pixel width of medullary cavity (MC) was also estimated. Furthermore, the study evaluated the performance of linear regression (LR) models for age estimation from radiographic images of proximal femora. Negative correlations were observed between FH, FN, WT, and GT pixel intensity with the age-at-death of the samples, with females exhibiting stronger correlations than males. Moreover, a positive correlation was found between age and MC width in female samples, while male MC widths did not show any relationship with increasing age. The results showed a slight difference between the LR model applied to both sexes, which integrated all variables, and the alternative configuration that only utilized relevant attributes. Both models exhibited similar performance, with a narrow range of root mean square error (RMSE) values, ranging from 12.67 to 12.71 years, and a correlation coefficient range of 0.51 to 0.52. For females, the LR model with FN and WT as selected attributes (RMSE = 11.85 years, correlation coefficient = 0.65) performed decently, while for males, the LR model with all variables showed RMSE of 12.52 years and correlation coefficient of 0.46. This study showcased the potential application of pixel intensity in predicting age. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Age estimation methods through the metric study of bones in non-adult individuals are a frequently used tool in Biological and Forensic Anthropology; however, few methodological validation studies are available, given the limited number of adequate samples for their study. In this context, the aim of this work is to test the effectiveness of the methods of Fazekas and Kósa (1978), Saunders et al. (1993), Rissech and Black (2007) and Cardoso et al. (2017), for age estimation through the measurements of the scapula, in the non-adult osteological collection of the Anthropology Laboratory of the University of Granada (Spain), one of the most representative identified non-adult collections internationally. For this purpose, nine different measurements of the scapula were taken from a total of 149 non-adult individuals, applying the regression formulae proposed by the different authors and verifying their accuracy. Results have shown the existence of significant differences between the real age and the estimated age in almost all the tested methods, with degrees of error that differ from those obtained in the original study populations. Based on the obtained results, it is recommended to use the method of Cardoso et al. in postnatal individuals, always adjusting the degrees of error with those obtained in this work. [ABSTRACT FROM AUTHOR]

Various staging scales have been proposed for the assessment of the visibility of the periodontal ligament space of mandibular third molars on dental panoramic radiographs (PANs) for forensic age assessment in living individuals. However, up to now, there has been no systematic comparison between these staging scales available. We directly compared the 2010 staging scale proposed by Olze et al. with the 2017 staging by Lucas et al. and the 2020 staging by Guo et al. in a German study population. We evaluated 233 PANs from 115 females and 118 males aged 20.0 to 40.9 years using three independent examiners, with one examiner conducting two assessments. We examined the correlation between age and stage, as well as the inter- and intra-rater reliabilities. While the point estimates for the correlation coefficient and the reliability measures were lowest for the Guo scale and highest for the Olze scale, confidence intervals showed a large overlap, particularly for the scales of Olze et al. and Lucas et al. The correlation coefficients between stage and age were consistently lower in females than in males across all methods. In summary, we showed that the staging scales of Olze et al. and Lucas et al. were very similar. The Olze method showed higher point estimates across all analyses, and because there are more reference data available for this method, we argue that it should be preferred as the method of choice for further studies in the field. However, Guo method could be considered for instances, in which the inter-radicular periodontal ligament is not evaluable. [ABSTRACT FROM AUTHOR]

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging. [ABSTRACT FROM AUTHOR]

BACKGROUND: Forensic odontology is a subfield of forensics that can assist in determining age, gender, and other demographic factors. OBJECTIVE: The focus of this research is to ascertain age by tooth volume ratio of permanent maxillary canines using a CBCT scan. METHODS: In this retrospective investigation, 1000 CBCT images were gathered and focused on only fully developed maxillary canines. For each of the maxillary canines, the researchers measured and calculated the volume of the pulp chamber, root canal, and entire tooth using the ITK Snap programme. The pulp volume to tooth volume ratio was computed. The equation AGE = 59.208 - (583.652 x pulp volume/tooth volume) was then used to compute age. The samples were divided into 5 age groups; 13–20, 21–30, 31–40, 41–50, and 51–60. The age was calculated for samples in each group and it was compared to the actual age. RESULTS: Between individuals' actual age and computed age, the study discovered a statistically significant positive association. While groups 3, 4, and 5 demonstrated positive but weaker correlations (r = 0.854, r = 0.658, and r = 0.612, respectively), groups 1 and 2 displayed statistically significant strong positive correlations (r = 0.987 and r = 0.923, respectively). The average total discrepancy between the age that was computed and the actual age was 2.45 1.344. CONCLUSION: The study found a statistically significant positive correlation between computed age and actual age, with the younger age groups 1 (13–20) and 2 (21–30) showing the strongest relationships. This underlines its potential success in court, particularly when dealing with young criminals. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the utility of Cone Beam Computed Tomography (CBCT)-based pulp tooth volume- ratio of maxillary anterior teeth for accurate age estimation. The project aimed to utilize the HOROS software for image analysis and develop prediction models using regression analysis. 1800 male patients in the age group of 20 to 40 years were selected, and maxillary anterior teeth were picked. High-resolution CBCT scans were collected, and image analysis in terms of pulp volume (PV), tooth volume (TV), and pulp-volume-to-tooth-volume ratio (PV/TV) was calculated using HOROS software. Simple linear regression analysis was used to develop prediction models correlating the PV/TV with chronological age. PV/TV of all teeth ranged between 0.073 and 0.214. Pearson correlation coefficient was used to evaluate the correlation between the chronological age and the PV/TV. It shows a statistically significant (positive) but low correlation between age and PV/TV 13 and 22 (combined), respectively, and the highest Pearson correlation (0.849) for maxillary canine (13). This study presents four models for age estimation with maximum standard error ranging between 3.5 and 4.3 and an accuracy of 96%. This study illustrates the effectiveness of CBCT-based PV/TV of maxillary anterior teeth for age assessment. Accurate prediction models were constructed by using regression analysis and the HOROS software. These findings enhance the study of forensic odontology and have potential applications in forensic investigations, archaeological research, and legal-age assessment. Further research is necessary to validate and refine the prediction models, expanding their applicability to larger and more diverse population samples. [ABSTRACT FROM AUTHOR]

Face biometrics play a crucial role in describing gender and age changes. Male and female faces differ in terms of smoothness due to the formative properties, nature of lifestyle, and use of cosmetics. For age progression, faces record different levels of face vitality - from smooth face within childhood to wrinkles and folds in senior adult faces. This paper presents a set of dispersion measures to represent face features over gender and age variation. Besides, we inspect the exchanging effects between gender differences and life stages on the yielded results. These dispersion features are represented using localized formulas of statistical measures, such as Standard Deviation (SD), Statistical Ratio (SR) and Roughness Value (RV). Such statistical measures inspect the differences of image values regarding specific reference. The SD inspects the differences between a set of values and their average, while SR gauges the SD for set of values normalized by their average. The RV measure inspects the difference between the successive values normalized by their differences from the average. This paper adopts the localized forms of these measures to avoid the effects of the challenges (illumination and rotation) that dataset images suffer from. These adopted features provide significant discriminating powers over different ages and genders. The yielded results show that the adopted measures recorded mutual effects between age estimation and gender recognition. [ABSTRACT FROM AUTHOR]

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks. [ABSTRACT FROM AUTHOR]

Introduction: Age estimation plays a vital role in various medico legal cases including both civil and criminal litigations. Assessment of age of an individual by examination of teeth is one of the universally accepted methods of age estimation. In the present study we observed the patterns of dental eruption in Coimbatore. In this study we considered the eruption pattern of all permanent teeth (Molar 1 teeth, Central Incisor teeth, Lateral Incisor teeth, Pre Molar 1, Pre Molar 2, Canine and Molar 2). Material and Methods: The present study was a community based cross sectional study conducted in C.R.R. Matric Higher Secondary School, Ondipudur. C.R.R. Mat.Hr.Sec. School (CMHSS) located at Coimbatore S.S. Kulam, Ondipudur, The study was conducted among 1000 study participants. Results: The mean age for complete eruption of the first molar teeth and central incisor was observed to be between 75.9 to 77.9 months and 80.8 to 84 months respectively. Lateral Incisor teeth was completely erupted among all the study participants aged between 105.2 and 107.4 months of age. The mean age for complete eruption of the first premolar was observed to be between 109.58 to 112.6 months. The mean age for complete eruption of the second pre molar was observed to be between 131.0 to 133.8 months. Complete eruption of Canine and second molar was observed to be between 142.6 to 144.8 months and 174.5 - 178.6 months of age respectively. Conclusion: First Pre Molar, Second Pre Molar, Canine and the second molar appears to be earlier on left side as compared to the left side. The eruption of the second molar tooth was significantly earlier in the female study participants as compared to the male study participants. The dental eruption patterns should be studied in detail to assess the role off various associated factors like nutrition, oral hygiene. [ABSTRACT FROM AUTHOR]

The featherback fish, Notopterus notopterus, is an important freshwater fish widely distributed in several water bodies all over the country. The present study describes the length-weight relationship and age determination from growth checks on scales for N. notopterus collected from Farhatabad nala of Gulbarga district of Karnataka, India. The length and weight showed an allometric growth pattern in either sex. The values of the exponent 'b' for N. notopterus are 2.6220 for males and 2.7175 for females, respectively. The coefficient of correlation 'r' for combined sex was found to be 0.909 (p<0.001). The total scale length vs. total length of fish showed a significant positive correlation (r = 0.9287, P<0.0001) in this fish. Based on the growth checks and annuli on scales, it can be concluded that up to age class 3, the growth is comparatively greater. The present study, therefore is ample proof of the authenticity of scales for age determination. Based on the length-weight relationship, it can be suggested that the fish N. notopterus of either sex is not showing a normal pattern of growth, i.e., an increase in weight is not proportional to the increase in length. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

Abstract Background Forensic anthropological examinations help in identification of unidentified human remains. This study aims to establish population-specific standards for age estimation from the sternal ends of left fourth ribs in the Nepalese population. A quantitative, observational study was conducted on 387 Nepalese deceased (106 females, 281 males) between November 2021 and March 2023. Left fourth ribs were collected, macerated and examined for the study. The variables examined included pit depth, pit shape, rim and wall configurations and rib phase. Results Pit depth significantly predicted age, β = 0.642, t(385) = 16.42, p

Abstract Background The aim of this study, which was based on this hypothesis, was to estimate sex and age by using a machine learning algorithm (ML) and artificial neural networks (ANN) with parameters obtained from the eyeball. The study was conducted on corneal topography images of 155 women and 155 men aged between 6 and 87 who did not have surgical intervention or pathology in their eyeballs. In the study, the individuals were divided into four different age groups 6–17, 18–34, 35–55, and 56–87. Sex and age estimation was carried out by using the numerical data of parameters obtained as a result of corneal topography imaging in ML and ANN inputs. Results As a result of our study, in sex determination, a 0.98 accuracy rate (Acc) was obtained with the logistic regression algorithm, one of the ML algorithms, and 0.94 Acc was obtained with the MLCP model, one of the ANN algorithms; in age estimation, 0.84 Acc was obtained with RF algorithm, one of the ML algorithms. With the SHAP analyzer of the Random Forest algorithm, through which the effects of parameters on the overall result are evaluated, the parameter that made the highest contribution to sex estimation was found to be corneal volume, and the parameter that made the highest contribution to age estimation was found to be pupil Q parameter. Conclusion As a result of our study, it was found that parameters obtained from the eyeball showed a high accuracy in sex and age estimation.

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.

Background: The aim of this study, which was based on this hypothesis, was to estimate sex and age by using a machine learning algorithm (ML) and artificial neural networks (ANN) with parameters obtained from the eyeball. The study was conducted on corneal topography images of 155 women and 155 men aged between 6 and 87 who did not have surgical intervention or pathology in their eyeballs. In the study, the individuals were divided into four different age groups 6–17, 18–34, 35–55, and 56–87. Sex and age estimation was carried out by using the numerical data of parameters obtained as a result of corneal topography imaging in ML and ANN inputs. Results: As a result of our study, in sex determination, a 0.98 accuracy rate (Acc) was obtained with the logistic regression algorithm, one of the ML algorithms, and 0.94 Acc was obtained with the MLCP model, one of the ANN algorithms; in age estimation, 0.84 Acc was obtained with RF algorithm, one of the ML algorithms. With the SHAP analyzer of the Random Forest algorithm, through which the effects of parameters on the overall result are evaluated, the parameter that made the highest contribution to sex estimation was found to be corneal volume, and the parameter that made the highest contribution to age estimation was found to be pupil Q parameter. Conclusion: As a result of our study, it was found that parameters obtained from the eyeball showed a high accuracy in sex and age estimation. [ABSTRACT FROM AUTHOR]

Considering that different regions and races have different skeletal development stages, it is imperative to adopt appropriate standards for the age assessment of specific populations. The study aimed to identify a suitable method for knee epiphysis grading and evaluate the feasibility of age estimation based on magnetic resonance imaging (MRI) and X-ray in the Chinese population. MRI images and X-ray images of the knee were collected from 1018 volunteers and 833 volunteers aged 6–20 years old, respectively. A modified Kramer's classification method was used for the experiment. The minimum, maximum, mean, and standard deviation of radiography and MRI were defined for males and females in each age group. The ossification process in knee joints of the female volunteers occurred earlier compared to the male, and the X-ray grading was higher than that of the MRI during the same period. In the regression analysis, the R2 value with age of MRI was generally higher than the X-ray in males. MRI can be used as an effective, reliable, non-invasive, and non-radiation method for evaluating bone age. This study verified the feasibility of applying the knee epiphysis grading for forensic investigations in Chinese population. [ABSTRACT FROM AUTHOR]

This paper presents a retrospective analysis of postmortem computed tomography (PMCT) scans of secondary ossification centers in the medial clavicular epiphysis, iliac crest apophysis, proximal humeral epiphysis, distal femoral epiphysis, proximal tibial epiphysis, and distal tibial epiphysis. At the same time, we analyzed PMCT scans of the maxillary and mandibular incisors, canines, premolars, and molars. We assessed 203 corpses, whose age ranged from 2 to 30 years, including 156 males and 47 females. The purpose of our study was to compare the processes of secondary ossification center fusion and permanent tooth maturation. Our research hypothesis was that certain stages of skeletal and dental maturation occur along consistent timelines that can be related to the chronological age. Secondary ossification center fusion was evaluated based on Kreitner and also McKern and Steward's classifications. The process of permanent tooth maturation was evaluated with Demirjian's method. Spearman's correlation coefficients (Rho) were positive in all analyses, which indicates that epiphyseal fusion progresses with age. The strongest relationship between the age and the stages of ossification was observed in the proximal tibial epiphysis (p < 0.001; Rho = 0.93) in females and in the medial clavicular epiphysis (p < 0.001; Rho = 0.77) in males. Studies show the importance of concomitant analysis of skeletal and dental maturation with a subsequent comparison of the results to achieve a greater precision in age estimation. A comparison of the results obtained in the study population of Polish children, adolescents, and young adults with the results of other studies in populations of similar ages showed a number of similarities in the time windows of dental and skeletal maturation. These similarities may help in age estimation. [ABSTRACT FROM AUTHOR]

The age estimation (AE) of human remains is a challenging task since it is dependent on the state in which these remains are found. Since the macroscopic evaluation of palatal sutures has been proposed as a method for AE, the aim of this study was to review the literature on this method, considering that the cases of edentulous elderly are among the greatest challenges in anthropological and forensic contexts. A scoping review was performed using a specific search strategy in PubMed, Web of Science, SciELO, LILACS, and Google Scholar. The search identified 13 articles, among which the USA yielded the most information with 3 articles. Only 1 study was identified in Latin America (Peru). There was great diversity regarding the origin of samples, and the studies were carried out on both historical and modern populations. Only 6 articles exceeded the average sample size (168.08) and 4 articles studied samples of fewer than 100 individuals. Although 6 different methods were identified, Mann et al.'s revised method was the most used. The selection of appropriate methods for AE depends on what skeletal elements are present and the general age of the specimens. Although evaluation of the obliteration of the palatal sutures has been found to be simple and promising for AE in individuals over 60 years of age, this method has been reported to have less precision than other more complex methods, which makes the use of a combination of methods necessary to increase the level of confidence and the percentage of success. Further research could resolve this weakness, and methodological refinement (perhaps the digitization and automation of processes, or the application of Bayesian methodology) could provide the necessary solidity to comply with international standards in the forensic scenario. [ABSTRACT FROM AUTHOR]