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Background:-Cementum (hard tissue of root), is deposited in layers around the dentin all throughout life. Root cementum is visible as alternate light and dark rings known as incremental lines of cementum under light microscopy. These annulations can be reliably used for age estimation in forensic science. Aim & Objective:-To determine age estimation of human skeleton by cemental annulations of extracted tooth. The primary objectives of the study were to count number of cemental annulation of freshly extracted human teeth after longitudinal cross-section and to compare the results with actual age of the patients. Materials & Method:-30teeth were selected and sectioned longitudinally. Sections were ground, mounted and viewed under a bright light microscope. The area selected for counting was photographed under 4X &10X objectives, cemental lines were counted and added to the eruption age of that patient, to obtain the chronological age. Results:-Mean estimated age (MEA) was 45.95 years and mean actual age (MAA) was 45.42 years. The difference in the values between actual and estimated age was seen to be varying from +6 years to -3 years (approximately 9 years difference). Standard deviation was 2.77, z-value of 0.5 and p-value was 0.62 which was not significant. Correlation co-efficient showed a value of 0.98, proving strong positive correlation between the both the groups. Conclusion:- The application of cemental annulations to determine age estimation suggestively serves as a valuable aid in forensic dentistry to identify the approximate age of an individual. [ABSTRACT FROM AUTHOR]

The interplay between facial age and facial identity is evident from several scenarios experienced in daily life, such as when recognising a face several decades after the last exposure. However, the link between age and identity processing, and how age perception abilities might diverge in individuals with different face processing abilities, has scarcely been considered. Furthermore, the approach used to test age perception ability may also influence outcome, but the effect of different paradigms on performance is not yet known. Across three studies, we compare super-recognisers (SRs), people with developmental prosopagnosia (DPs), and a group of neurotypical controls, on three age perception paradigms. There were no differences on the numeric age estimation task (i.e. providing precise age estimates for a series of faces; Study 1), and numeric age estimation task with added noise-distortion to stimuli (Study 2). However, SRs were more accurate when instructed to classify ambient faces as either over- or under- the age of 18 compared to both DPs and controls (Study 3). Thus, there may be nuanced differences in age processing which can be tapped into using separate paradigms; however, given that the difference is only with SRs it remains unclear to what extent these are linked to facial identity processing. [ABSTRACT FROM AUTHOR]

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep‐learning model for automated age prediction using panoramic dental X‐ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X‐ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine‐tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10–20]) to 13.40 years (age group [90–100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real‐world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios. [ABSTRACT FROM AUTHOR]

Age-at-death estimation is an important issue in forensic medicine and anthropology. Initially, methods relied on morphological criteria, but with the advancement of radiology, new techniques such as morphological studies on multi-slice computed tomography (CT) reconstructions have emerged. Recent studies have shown promising results by investigating the correlation between age and bone mineral density (BMD). However, there is currently a lack of data on post-mortem CTs (PMCT) involving decomposed bodies, and limited information exists regarding changes in Hounsfield Units measurement in a post-mortem context. In light of these gaps, our study aimed to examine the relationship between age at death and pubic and ilium BMD using a sample of forensic bodies. We also aimed to determine whether post-mortem processes, such as putrefaction, could interfere with this correlation. Our retrospective analysis encompassed 637 PMCTs conducted before medicolegal autopsies at Tours University Hospital. Utilizing simple and multiple linear regressions, we investigated the correlation between age and pubic and ilium BMD, as well as the relationship between BMD and the radiologic alteration index (RAI), a scale employed to quantify the degree of putrefaction. Our findings indicate promising outcomes in age-at-death estimation using pubic and/or ilium BMD for bodies exhibiting no or moderate decomposition (RAI < 80), particularly among individuals under 40 years old. However, for highly decomposed corpses (RAI ≥ 80), the presence of gas infiltration significantly influences the BMD of both the ilium and pubis. Consequently, we advocate for the incorporation of the RAI score into the age estimation equation to enhance the accuracy of our results in such cases. Further investigation involving a larger cohort of decomposed bodies could facilitate refinement and validation of our method within this specific population. [ABSTRACT FROM AUTHOR]

Necrophagous flies, particularly blowflies, serve as vital indicators in forensic entomology and ecological studies, contributing to minimum postmortem interval estimations and environmental monitoring. The study investigates variations in the predominant cuticular hydrocarbons (CHCs) viz. n-C25, n-C27, n-C28, and n-C29 of empty puparia of Calliphora vicina Robineau-Desvoidy, 1830, (Diptera: Calliphoridae) across diverse environmental conditions, including burial, above-ground and indoor settings, over 90 days. Notable trends include a significant decrease in n-C25 concentrations in buried and above-ground conditions over time, while n-C27 concentrations decline in buried and above-ground conditions but remain stable indoors. Burial conditions show significant declines in n-C27 and n-C29 concentrations over time, indicating environmental influences. Conversely, above-ground conditions exhibit uniform declines in all hydrocarbons. Indoor conditions remain relatively stable, with weak correlations between weathering time and CHC concentrations. Additionally, machine learning techniques, specifically Extreme Gradient Boosting (XGBoost), are employed for age estimation of empty puparia, yielding accurate predictions across different outdoor and indoor conditions. These findings highlight the subtle responses of CHC profiles to environmental stimuli, underscoring the importance of considering environmental factors in forensic entomology and ecological research. The study advances the understanding of insect remnant degradation processes and their forensic implications. Furthermore, integrating machine learning with entomological expertise offers standardized methodologies for age determination, enhancing the reliability of entomological evidence in legal contexts and paving the way for future research and development. [ABSTRACT FROM AUTHOR]

Cranial vault thickness (CVT) and its variations provide valuable insights into an individual's biological attributes such as age and sex. This paper aimed to assess the correlations of CVT with age and sex and develop a regression model for age estimation in Malaysian subadults using computed tomography (CT) images. A total of 521 CT images (male/female: 279/242; age range: 0–20 years; Malay/Chinese/Indian: 221/145/155) were included in the study. Correlations of CVT measurements with age and sex, including frontal bone thickness (FBT), occipital bone thickness (OBT), left parietal bone thickness (LPBT), and right parietal bone thickness (RPBT) were assessed and regression formulae were developed for age estimation in subadults. A significant correlation between CVT measurements and age was demonstrated (p < 0.001). Age estimation was most accurate in the younger age group (< 2 years) at frontal and occipital, and accuracy decreases in the older age groups. Additionally, sexual dimorphism was evident in the frontal and parietal bone thickness within the age range of 3–6 years and 16–20 years, respectively. In conclusion, the findings suggested CVT measurements could be used to corroborate other age estimation methods for subadults. [ABSTRACT FROM AUTHOR]

The age estimation task aims to use facial features to predict the age of people and is widely used in public security, marketing, identification, and other fields. However, the features are mainly concentrated in facial keypoints, and existing CNN and Transformer-based methods have inflexibility and redundancy for modeling complex irregular structures. Therefore, this paper proposes a multi-view mask contrastive learning graph convolutional neural network (MMCL-GCN) for age estimation. Specifically, the overall structure of the MMCL-GCN network contains a feature extraction stage and an age estimation stage. In the feature extraction stage, we introduce a graph structure to construct face images as input and then design a multi-view mask contrastive learning (MMCL) mechanism to learn complex structural and semantic information about face images. The learning mechanism employs an asymmetric Siamese network architecture, which utilizes an online encoder–decoder structure to reconstruct the missing information from the original graph and utilizes the target encoder to learn latent representations for contrastive learning. Furthermore, to promote the two learning mechanisms better compatible and complementary, we adopt two augmentation strategies and optimize the joint losses. In the age estimation stage, we design a multi-layer extreme learning machine (ML-IELM) with identity mapping to fully use the features extracted by the online encoder. Then, a classifier and a regressor were constructed based on ML-IELM, which were used to identify the age grouping interval and accurately estimate the final age. Extensive experiments show that MMCL-GCN can effectively reduce the error of age estimation on benchmark datasets such as Adience, MORPH-II, and LAP-2016. [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]

Objective: The present study aims at studying the ossification of thyroid cartilage and to compare it with age of the individual. Methods: The study was conducted in 300 samples of thyroid cartilage collected during autopsy in the department of Forensic medicine in a tertiary care hospital. Each thyroid cartilage is divided in to thirteen anatomical area and ossification in each defined area was studied through gross and cut section features and classified as ossified and not ossified areas. The samples were grouped in to seven groups of age and ossification features were compared between the age groups. Results: On analysing the ossification pattern of thyroid cartilage in thirteen defined anatomical areas and comparing it with the age, a peculiar sequence of ossification was noted, and it was also observed that frequency of ossification increases significantly with increasing age groups. Conclusion: Ossification of thyroid cartilage helps in establishing the age of individual and it can be categorised to an age group of ten years. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the suitability of three age estimation methods, using different radiological techniques, for estimating the chronological age (CA) of children living in the Mediterranean region, and whether these methods can be used in forensic procedures. Method: A total of 1296 digital orthopantomographic and hand-wrist radiographs of 648 children aged between 7 and 16 years were evaluated. Fishman, Willems and Nolla age estimation methods were used to estimate CA. Results: Chronological and dental age correlated with the Nolla and Willems methods (NM and WM) in both boys and girls. There was no statistically significant difference between upper canine age estimation and CA using the NM in children aged 7-11 years in boys and in girls. There was no statistically significant difference between the NM upper canine age estimation and CA in children aged 12-14 years in boys. Conclusion: The WM may be suitable for estimating CA in boys and girls aged 7 and 14 years. The Fishman method may be suitable for estimating CA in girls aged 12-14 years. The NM of upper canine age estimation can also be used to estimate CA.

Abstract Biological age is an indicator of whether an individual is experiencing rapid, slowing, or normal aging. Perceived age is highly correlated with biological age, which reflects health appraisal and is often used as a clinical marker of aging. Perceived age has been reported as an important indicator of biological age and general health status, not only in older adult populations but also in young and middle-aged adults. However, there is a lack of objective methods for quantifying perceived age in these younger age groups. Thus, this study aimed to propose a novel perceived age estimation algorithm to meet the need for an objective method to predict perceived age. This cross-sectional study included 609 healthy men and 1388 healthy women (29.02–57.91 years, average 44.4 years) from 2017 to 2019 using data from the Korean Medicine Daejeon Citizen Cohort Study. The proposed algorithm comprised two steps. First, the initial predicted perceived age was estimated from facial images using a convolutional neural network (CNN) ensemble model. Then, the final perceived age was estimated using regression from the chronological age, sex, BMI, and initial predicted perceived age obtained in the first step. Better performance results were obtained by model averaging and model stacking generated from various basic regression models. The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.

Abstract The interplay between facial age and facial identity is evident from several scenarios experienced in daily life, such as when recognising a face several decades after the last exposure. However, the link between age and identity processing, and how age perception abilities might diverge in individuals with different face processing abilities, has scarcely been considered. Furthermore, the approach used to test age perception ability may also influence outcome, but the effect of different paradigms on performance is not yet known. Across three studies, we compare super-recognisers (SRs), people with developmental prosopagnosia (DPs), and a group of neurotypical controls, on three age perception paradigms. There were no differences on the numeric age estimation task (i.e. providing precise age estimates for a series of faces; Study 1), and numeric age estimation task with added noise-distortion to stimuli (Study 2). However, SRs were more accurate when instructed to classify ambient faces as either over- or under- the age of 18 compared to both DPs and controls (Study 3). Thus, there may be nuanced differences in age processing which can be tapped into using separate paradigms; however, given that the difference is only with SRs it remains unclear to what extent these are linked to facial identity processing.

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.

Background: The concept of comparing “chronological age” vs. that of “dental age” has been well documented. Considering ethnic variations in dental development, the use of population-specific standards for age estimation has been emphasized. Mandibular maturity markers have been successfully employed to estimate the age of a person at the 18-year threshold. No such data are available for the Chinese population, which represents around one-fifth of the global human population. Aim: The aim of this research project was to analyze and grade mandibular third molars using maturity markers in the Chinese population. Materials and Methods: In total, 882 panoramic radiographs (424 females and 458 males) of 16- to 24-year-old Chinese children and young adults were obtained from patient archives. All patients were graded by a trained and calibrated examiner using stages A-D for mandibular maturity markers including Periodontal Ligament Visibility (PLV) and Root Pulp Volume (RPV). Results: The average Age at Assessment (AaA) ranged from 18.79 years for females in Stage A of PLV to 21.59 years for females in Stage D of PLV. The AaA was not statistically significant between females and males across all the stages, for both PLV and RPV. For PLV, the percentage of females below 18 years was 57.14% in Stage A and 17.14% in Stage D. For males, it was 48.84% in Stage A and 14.93% in Stage D. For RPV, the percentage of females below 18 years was 54.72% in Stage A and 12.73% in Stage D. For males, it was 49.09% in Stage A and 14.81% in Stage D. On average, for PLV, 85.30% of subjects were over 18 years of age in Stages C and D, and for RPV, 82.50% subjects were over 18 years of age in Stages C and D. Conclusions: Both RPV and PLV are considered as reliable maturity markers to estimate the age of Chinese subjects in the 18-year threshold. No difference was observed in the average Age at Assessment of mandibular maturity stages between the sexes.

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

Background Forensic age estimation is not difficult when the body is found in good condition, but in cases of severe decomposition or damage, such as burnt or separated body parts, then the analysis can only be done with bones and teeth. There has been abundant research and development in the field of related biochemistry over the years. Various molecular changes occur in hard tissues and long-lived proteins, such as those in bones and teeth during the physiological process of aging. Aspartic acid racemization and DNA methylation are still the most frequently used age estimation methods because of their advantages in accuracy. Method This study aimed to compare the accuracy of DNA methylation and aspartic acid racemization methods for age estimation. Journal articles were searched in the electronic databases PubMed, Scopus, and Semantic Scholar of 2017-2022 according to PRISMA guidelines. Result Twelve journal articles were eligible for review. The DNA methylation method is quite simple to use because of commercially available methylation kits. Furthermore, the results can be obtained relatively quickly without requiring many samples, and the method is less sensitive to thermal and other damage than the aspartic acid racemization method. Conclusion The aspartic acid racemization method for age estimation has high accuracy, especially in determining age at death. However, temperature and the condition of the teeth affect the racemization of aspartic acid. Given that DNA methylation is generally stable in a wide range of temperatures, it provides a better approach to determining the chronological age even from charred remains.

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

As the teeth are more durable than other parts of the skeleton, they provide valuable data for age estimation. Age estimation from adult teeth is mainly based on secondary dentin production. The present study aimed to devise a regression formula for age estimation specific to the Anatolian population using the Kvaal method on CBCT reconstructed panoramic images. In total, 201 individuals aged between 20 and 69 were divided into two groups: data from the study group (n = 101) were used to create the regression formulae, and data from the control group (n = 100) were used to test the formulae. Pearson's correlation coefficients and linear regression analyses were performed. Maxillary teeth provided more accurate age estimates than mandibular teeth. The regression formulae derived in this study are found to be statistically applicable and reasonably accurate. However, these results should be interpreted with caution. [ABSTRACT FROM AUTHOR]

The study investigated the relationship between the histological compositions of the tricuspid, pulmonary, mitral, and aortic valves, and age. All 85 fresh human hearts were obtained with an age range between 20 and 90 years. The central area of the valves was conducted to analyze the density of collagen and elastic fibers by using an image analysis program. Neural network function in MATLAB was used for classification data and accuracy test of the age predictive model. Overall, a gradual increase in the density of collagen and elastic fibers was demonstrated with age in all valve types. The pulmonary valve cusps had the least density of collagen and elastic contents, whereas the most dense of collagen was found in the mitral leaflets. A similarity was noted for the elastic fibers in the tricuspid, mitral, and aortic valves. The highest correlation between the collagen (r = 0.629) and elastic fibers (r = 0.713) and age was found in the noncoronary cusp of the aortic valve. The established predictive equations using collagen and elastic fibers in the noncoronary cusp provided the standard error of ± 14.0 and 12.5 years, respectively. A 60.9% of accuracy was found in all age groups using collagen, while accuracy in elastic fibers showed 70.0% in the classification process using the neural networks. The current study provided additional data regarding age-associated changes of collagen and elastic fibers in the human heart valves in Thais and the benefits and application in age forensic identification. [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 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]

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Background: The concept of comparing "chronological age" vs. that of "dental age" has been well documented. Considering ethnic variations in dental development, the use of population-specific standards for age estimation has been emphasized. Mandibular maturity markers have been successfully employed to estimate the age of a person at the 18-year threshold. No such data are available for the Chinese population, which represents around one-fifth of the global human population. Aim: The aim of this research project was to analyze and grade mandibular third molars using maturity markers in the Chinese population. Materials and Methods: In total, 882 panoramic radiographs (424 females and 458 males) of 16- to 24-year-old Chinese children and young adults were obtained from patient archives. All patients were graded by a trained and calibrated examiner using stages A-D for mandibular maturity markers including Periodontal Ligament Visibility (PLV) and Root Pulp Volume (RPV). Results: The average Age at Assessment (AaA) ranged from 18.79 years for females in Stage A of PLV to 21.59 years for females in Stage D of PLV. The AaA was not statistically significant between females and males across all the stages, for both PLV and RPV. For PLV, the percentage of females below 18 years was 57.14% in Stage A and 17.14% in Stage D. For males, it was 48.84% in Stage A and 14.93% in Stage D. For RPV, the percentage of females below 18 years was 54.72% in Stage A and 12.73% in Stage D. For males, it was 49.09% in Stage A and 14.81% in Stage D. On average, for PLV, 85.30% of subjects were over 18 years of age in Stages C and D, and for RPV, 82.50% subjects were over 18 years of age in Stages C and D. Conclusions: Both RPV and PLV are considered as reliable maturity markers to estimate the age of Chinese subjects in the 18-year threshold. No difference was observed in the average Age at Assessment of mandibular maturity stages between the sexes. [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]

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]

Age estimation from facial images is crucial in security, healthcare, and entertainment for applications like age-restricted content filtering and targeted advertising. However, this task is challenging due to pose, illumination, and aging variations. Traditional methods using handcrafted features often lack discriminative power. Deep learning, with its ability to learn complex features, has sparked interest in age estimation. This paper presents a deep learning-based approach for age estimation from frontal face images. It focuses on facial components like eyes, nose, and mouth to capture age-related changes. Augmentation techniques enhance robustness against variations, and a Binary Cascaded CNN with binary weights and activations reduces model complexity. The model transforms augmented components into multimodal features, allowing it to discern age-related changes across domains. Evaluated on FGNET and IMDB-WIKI datasets, the model achieves 99.5% accuracy with an MAE of 1.26 across all age groups. This high accuracy demonstrates the model's effectiveness and potential for real-world age estimation scenarios in diverse applications. [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]

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]

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 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]

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]

Objective: The aims of this study were to assess the accuracy of Cameriere’s and Demirjian’s methods in Finnish children, and compare the findings with those of the Turkish children according to dental age. Material and methods: Dental panoramic tomography (DPT) of children (482 Finnish, 423 Turkish) aged between 5 and 15 years were evaluated. Comparison of mean difference between estimated and chronological age was evaluated. The difference between two means was analysed using paired t-test at 95% confidence interval (CI). Pearson correlation coefficients were used to estimate the correlation between chronological and estimated ages. Results: Demirjian’s method resulted in overestimation in all age groups except for 8-year-old girls. Dental age, however, was found to be underestimated with Cameriere’s method in all age groups but 6-year-old girls and boys. In Northern Finnish children, Demirjian’s method was more suitable for boys while Cameriere’s method led to better estimation in girls. When comparing Finnish and Turkish children, differences between dental ages and chronological ages differed significantly in 10-year-old boys and 8-year-old girls with both methods. Conclusion: Dental age of Turkish children seems higher than that of Finnish children. There is a significant difference between chronological and dental ages in both populations assessed by both methods.

This study aimed to analyze the growth parameters and age-length relationships of the sandbird octopus (Amphioctopus aegina) along the coast of Chao Samran Beach, Phetchaburi Province, Thailand. This included determining reproductive stages, the gonadosomatic index, and size at first maturity. A total of 1436 specimens comprising 782 males and 654 females were collected using octopus traps during January to December 2021. The mantle length and weight of each specimen were measured. Reproductive stages were assessed to determine the gonadosomatic index and size at first maturity, and growth parameters were estimated using the von Bertalanffy growth equation. Results showed that males exhibited a mantle length range of 3.1–7.5 cm (mean: 5.2 ± 0.7) and a weight range of 5.99–94.00 g (mean: 35.34 ± 11.24). Mantle length at first maturity for males was 4.2 cm, with full maturity at 7.0 cm. Females had a mantle length range of 2.8–9.4 cm (mean: 5.7 ± 0.9) and a weight range of 7.70–105.00 g (mean: 45.43 ± 17.00). Mantle length at first maturity for females was 5.9 cm, with full maturity at 7.5 cm. GSI values were highest in both sexes in February, April, May, June and November, suggesting reproduction occurred through much of the year, and females showed a large peak in May. GSI values were lowest in both sexes in September. The smallest recorded mantle length was 2.8 cm in May, and the largest was 9.4 cm in January. The specific values of t0 for males, females, and both combined sexes were −0.017719, −0.014519, and −0.00672, respectively. These findings provide valuable insights into the growth and reproductive biology of A. aegina, which can inform sustainable management and conservation efforts for this species.

Objective: Cementochronology is a method for assessing chronological age and identifying other life-history parameters (LHPs) from incremental lines of acellular extrinsic fibre cementum (AEFC) in most mammalian teeth. The aim of this study is to question the accuracy of this technique when used as a stand-alone age estimation method, and to examine how the number of observers may alter accuracy. Design: This research is based on an extant clinical study conducted on 10 human teeth with the patients’ anamnestic data. Nine observers used cementochronology to count AEFC incremental lines from 82 digital images. The counting was performed at three non-standardised areas on each image, totalling 246 counts per observer. Resultant observer counts were compared using the coefficient of variation method. Results: The mean deviation of cementum estimated age from known chronological age of the participants in the study is 5.2 years. Conclusion: Our study shows that further critical examination of the current cementochronology technique is essential, due to the subjectivity of line counting. The number of skilled observers in the study may improve the overall accuracy of the technique. These issues have wider implications, as many researchers rely on accurate scientific inferences being made by cementum-based studies to support or refute overarching demographic models and grand evolutionary narratives grounded by life history theory. Until this issue is resolved cementochronology should only be used alongside other age estimation methods.

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