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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]

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]

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]

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]

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]

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]

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]

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp–tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise. [ABSTRACT FROM AUTHOR]

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 <60 years, with an area under the ROC curve (AUC) value of 0.751. Ovarian weights of 2.27 g/m2 and 1.92 g/m2 were the cutoff values for classifying the age as ≥40 years or <40 years, or ≥50 years or <50 years, with AUC values of 0.935 and 0.930, respectively. These methods can help determine an unknown individual's age group simply and quickly, even for incomplete cadavers. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines. [ABSTRACT FROM AUTHOR]

Simple Summary: This review introduces different methods for estimating the age of vertebrates for professionals in related fields and evaluates the accuracy of each method. Firstly, the degree of damage to vertebrates at the time of sample collection is classified. Then, the sample types and their applications are introduced, the most commonly used methods are listed, and methodological recommendations regarding accuracy are presented. Finally, the methods that will be used for vertebrate age estimation in the future, taking into account the current level of research and requirements, are predicted. This review is essential for promoting animal conservation and guiding the successful implementation of conservation management plans. Age is a core metric in vertebrate management, and the correct estimation of the age of an individual plays a principal role in comprehending animal behavior, identifying genealogical information, and assessing the potential reproductive capacity of populations. Vertebrates have a vertebral column and a distinct head containing a developed brain; they have played an important role in the study of biological evolution. However, biological age estimations constantly exhibit large deviations due to the diversity of vertebrate taxon species, sample types, and determination methods. To systematically and comprehensively understand age estimation methods in different situations, we classify the degree of damage to vertebrates during sample collection, present the sample types and their applications, list commonly applied methods, present methodological recommendations based on the combination of accuracy and implementability, and, finally, predict future methods for vertebrate age assessments, taking into account the current level of research and requirements. Through comprehensive data gathering and compilation, this work serves as an introduction and summary for those who are eager to catch up on related fields and facilitates the rapid and accurate selection of an evaluation method for researchers engaged in related research. This is essential to promote animal conservation and guide the smooth implementation of conservation management plans. [ABSTRACT FROM AUTHOR]

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

This review article will present an overview of biological profiles in forensic utilities. The biological profile of the skull in the existing literature can help to identify humans, especially if the condition of the victim found is a result of mutilation or a bomb explosion. When it comes to the precision of identifying skeletal remains, the human skull is frequently cited as being first in the estimation of age and ancestry and second in terms of sex and stature. It can be an alternative to assessing the following biological parameters: sex, age, stature, and ancestry. The implementation of biological profiles in the identification process is very important considering that some cases require the assistance of forensic anthropology. This review article shows the importance of the value of skulls. The method that can be applied is craniometry which can be used to determine sex, age, stature, and estimated ancestry. Different results will occur depending on the completeness of the skull. Therefore, estimation formulas have different accurate results. Discriminant function analysis has been performed on various measurement sets and its discriminant power has been validated by many researchers. Geometric morphometric analysis has become the main tool for shape analysis and many attempts have been made to use it in analyzing skulls. Several methods supported by technology have also been developed. It is hoped that the review article will show significant differences in results between studies in Thailand and Indonesia, even though they are in the same racial group. [ABSTRACT FROM AUTHOR]