Age Estimation Model Research Articles

Computed tomography study of cranial vault thickness in Malaysian subadult population.

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.

Forensic age estimation in adults based on multidetector computed tomography analysis of bone density in the medial meta-epiphyseal region of clavicle.

The aim of this study was to investigate the potential of using multidetector computed tomography (MDCT) to measure the bone mineral density (BMD) in the medial meta-epiphyseal region of clavicle (MERC) for adult age estimation. A total of 1064 chest MDCT scans from individuals aged 21 to 102years were utilized to determine the MERC BMD. The Mimics software was used for the BMD measurements, and the average BMD of both MERC was also calculated. Regression analysis was conducted with chronological age as a dependent variable and MERC BMD as an independent variable to establish a mathematical model for age estimation. The mean absolute error (MAE) was calculated to evaluate the accuracy of the regression model using an independent validation sample. Among all the models, the cubic regression model showed the highest correlation between MERC BMD and chronological age and also provided the most accurate age prediction for both males and females (MAE = 9.41 for males, MAE = 10.38 for females). Our study suggests that BMD measured by MERC can be utilized for age estimation in adults when more reliable indicators are not available.

An age-group ranking model for facial age estimation

Age prediction has become an important Computer Vision task. Although this task requires the age of an individual to be predicted from a given face, research has shown that it is more intuitive and easier for humans to decide which of two individuals is older than to decide how old an individual is. This work follows this intuition to aid the age prediction of a face by exploiting the age information available from other faces. It goes further to explore the statistical relationships between facial features within age groups to compute age-group ranks for a given face. The resulting age-group rank is low-dimensional and age-discriminatory, thus improving age prediction accuracy when fed into an age predictor. Experiments on publicly available facial ageing datasets (FGnet, PAL, and Adience) reveal the effectiveness of the proposed age-group ranking model when used with traditional Machine learning algorithms as well as Deep Learning algorithms. Cross-dataset validation, a method of training and testing on entirely different datasets, was also employed to further investigate the effectiveness of this method.

Utility of the morphological scoring of costal cartilage ossification in age estimation of adult Egyptians using multidetector computed tomography

Abstract Age estimation of adults is a challenging procedure in forensic practice. Inspired by the previous work by Chinese scholars, we established population specific age estimation models from the osseous and calcified projections (OCP) of costal cartilages, using three-dimensional volume-rendering technique. A total of 168 clinical CT scans (2 mm slice thickness) were used to develop the sex-specific age prediction models from a sample of Egyptians, comprising 70 females and 98 males, with documented ages between 12 and 85 years. The sample was also used for validating the Chinese model. We reported the differences between the predictive accuracy of the Egyptian (population specific) and Chinese (non population specific) models. The most accurate age estimation model was stepwise linear regression with standard error of estimates (SEE) of 10.9 and 11.8 years in males and females, respectively. For the simple linear regression models, the most accurate formula included OCP of the right second costal cartilage in males and OCP of the left third costal cartilage in females with SEE of 11.2 and 12.2 years, respectively and mean absolute error (MAE) of 8.8 and 9.6 years, respectively. By comparison, the best accuracy rates produced by the Chinese vs. the Egyptian models in males and females within 5 years were 30.61% and 32.86% vs. 35.71% and 32.86%, respectively, whereas within 10 years, the accuracy rates increased up to 57.14% and 58.57% vs. 72.45% and 64.29%, respectively. Although the accuracy rates from the Chinese models were lower than those obtained from the Egyptian models, the MAE and least error values were comparable in both sexes. Notable accurate age estimation rates in the advanced age group ≥40 years were reached being 81.25% to 97.92% in males and 69.77% to 93.02% in females. OCP of the right first costal cartilage was the most accurate in cross-population application for males and females with MAE values of 10.7 and 11.03 years, respectively with balanced accuracy rates of age estimation using the 10-year interval and 40-year cutoff.

An intelligent bone age assessment model incorporating multilayer superimposed texture enhancement and the China-05 attention mechanism

In the process of intelligent bone age assessment for Chinese ethnicity, generalized convolutional network models are not well targeted in extracting specific features in medical skeletal images and lack specificity in training and predicting skeletal developmental features in different ethnicities. This study aims to propose a hybrid improved deep residual network model, ZH05-DL-ResNet50, focusing on intelligent bone age assessment for Chinese ethnicity. In the process of building the ZH05-DL-ResNet50 model. First, multiple overlapping texture enhancement layers are introduced through combinatorial optimization, which can better characterize the global features of hand bone radiographs while using less texture information, reducing the interference of redundant information and freeing up computational power; second, the China-05′s spatial focusing mechanism is designed so that the model can intelligently focus on the region of interest, efficiently locate and automatically learn key image information; Finally, the model can be used for intelligent bone age assessment of Chinese ethnicity by constructing a 50-layer residual depth network, the superposition enhancement layer and the focusing mechanism are fused to form the ZH05-DL-ResNet50 model, which mitigates the problem of gradient disappearance and explosion caused by the increase of network depth, and reduces the information loss and depletion of the multi-layer texture superposition features in the focus region of interest. The training was performed using the Chinese ethnographic dataset provided and created by Tsinghua Changgeng Hospital, where 10 % of the radiographs were used as the test set and the rest as the training and validation sets. The overall performance of the models was estimated by comparing the average accuracy and loss values of different models, and the overall performance between model training and model design was evaluated using the data distribution (Distribution) and histogram of bias weight distribution (Histogram). The results showed that the average accuracy of the bone age estimation model was 98.1 % and the average error on the test set was 0.312 years. Evaluation of the model visualization and accuracy comparison showed that the model performed well and was more appropriate and accurate than other bone age estimation models. Therefore, the first intelligent bone age assessment model for Chinese ethnicity, ZH05-DL-ResNet50, was created, which differs from the untargeted nature of other intelligent bone age assessment models, and is used to train and predict hand bone radiographs more appropriately and accurately for Chinese ethnicity.

The Brazilian (FRANCO) method for dental age estimation: Willems' model revisited.

This study aimed (I) to test the Willems' dental age estimation method in different geographic samples of the Brazilian population, and (II) to propose a new model combining the geographic samples in a single reference table of Brazilian maturity scores. The sample consisted of 5017 panoramic radiographs of Brazilian males (n = 2443) and females (n = 2574) between 6 and 15.99 years (mean age = 10.99 ± 2.76 years). The radiographs were collected from the Southeastern (SE) (n = 2920), Central-Western (CW) (n = 1176), and Southern (SO) (n = 921) geographic regions. Demirjian's technique was applied followed by Willems' method and the proposed Brazilian model. Willems' method led to mean absolute errors (MAE) of 0.79 and 0.81 years for males and females, respectively. Root mean squared errors (RMSE) were 1.01 and 1.03 years, respectively. The Brazilian model led to MAE of 0.72 and 0.74 years for males and females, respectively, and RMSE of 0.93 years for both sexes. The MAE was reduced in 70% of the age categories. Differences between regions were statistically (p < 0.05) but not clinically significant. The new model based on a combined population had an enhanced performance compared to Willems' model and led to reference outcomes for Brazilians. Assessing patients' biological development by means of dental analysis is relevant to plan orthopedic treatments and follow up. Having a combined-region statistic model for dental age estimation of Brazilian children contributes to optimal age estimation practices.

A semen-specific deoxyribonucleic acid methylation model for epigenetic age estimation and its robustness under environmental challenges.

In forensic investigations, semen samples are a common form of biological evidence, especially in cases involving sexual assault. Therefore, accurately estimating the age of an individual is crucial in criminal cases. This study presents a novel age estimation model based on semen-specific CpG methylation patterns. A multiplex panel was developed, consisting of 12 CpG sites (PARP14, C5orf25, cg23488376, MXRA5, PFKFB3, DLL1, NOX4, cg12837463, TTC7B, KCNA7, NKX2-1, and SYNE4), which exhibit strong correlations with age. Additionally, this study investigates the resilience of these methylation markers under simulated environmental challenges. We collected ejaculate samples from a diverse cohort of 115 male individuals, aged 20-71 years, who underwent deoxyribonucleic acid extraction and bisulfite conversion. Methylation levels of the selected CpG sites were assessed using a SNaPshot assay, which revealed significant correlations with chronological age. We developed and validated two robust age estimation models through stepwise and enter regression analyses, achieving reliable accuracy with mean absolute errors ranging from 3.81 to 4.1 years. Additionally, the study also investigated the robustness of semen stains under diverse environmental conditions, including fabric type, washing, hematin exposure, and UV-C light. The selected methylation markers demonstrated remarkable resilience despite the challenges posed by washing procedures and environmental exposure, confirming their potential for age estimation in forensic genetics. This research presents successful age estimation models, emphasizing the strong correlations between methylation levels and chronological age. The proposed methodology's accuracy is affirmed through model validation on an independent test set, while also highlighting the resilience of semen stains on fabrics under varying storage and washing conditions.

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

Addressing Demographic Bias in Age Estimation Models through Optimized Dataset Composition

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.

AGE ESTIMATION USING DIAPHYSEAL LONG BONE LENGTHS IN A NON-ADULT SKELETAL SAMPLE FROM GONUR DEPE, TURKMENISTAN

Introduction. The aim of this study is to provide group-specific regression equations for age estimation of immature human skeletal remains younger than 12 years of age from the diaphyseal length of the six long bones. Further, to compare inverse and classical calibration models for age estimation and the suitability of previously published regression methods for archaeological and present-day populations in relation to the analyzed sample. Materials and methods. The studied sample consists of 128 non-adult individuals from Gonur Depe – the major BMAC site in Turkmenistan (2300–1500 BCE). Regression formulae were obtained for each of the six long bones, separately for entire sample (0–12 years of age) and for two subsamples below and above 2 years of age. For each equation the coefficient of determination (R2) and F-statistics were calculated. Residuals were tested for normality of distribution, autocorrelation, homoscedasticity, and the equality of mean to zero (one-sample t-test). Comparison between inverse and classical calibration was provided using paired samples t-test. To assess the applicability of other regression formulae to the studied sample both the mean residuals (MR) and mean of the absolute value of the residuals (MAR) were calculated, as an estimate of bias and accuracy respectively. Results. All regression models showed a strong statistical significance and high R2 value. The slope coefficients of the regression lines of diaphyseal length upon age are greater for the upper limb bones both in the entire sample and two subsamples separately. The lower limb bones are characterized by lower growth rates. In contrast to the inverse calibration, for the classical model the mean standard errors (MSE) were smaller for the upper limb bones rather than for the lower limb bones. For the lower limb bones the standard error of the estimate (SE) was generally smaller in inverse formulae. Comparison of both models for the femur however shows their equal performance. For the exception of proposed and classical calibration formulae all inverse models for femur diaphyseal length show consistent differences from zero in relation to the studied sample. Discussion. The inverse and classical calibration models as a technique for age estimation using diaphyseal long bone lengths are both equally applicable in the studied sample. It is recommended to use the equations for the lower limb bones in the inverse model and for the upper limb bones in the classical model. The Gonur Depe population is characterized by relatively higher growth rates of the upper limb bones and distal limb segments relative to the proximal ones. Most of the previously published inverse calibration models are not recommended for uncritically use due to the high risk of obtaining biased estimates on samples that are different chronologically and/or territorially.

Population specific dental age estimation model for South Indian children based on normalized open apices method: A cross sectional study

Age estimation plays significant role in forensic science especially when there is unavailability of the information concerned to the deceased. Use of population specific models of age estimation results in questionable evidence legally when tried on Indian children. The present study was designed to assess the accuracy of Cameriere’s Italian and Balwant Rai Indian model on South Indian children and to derive and validate a novel model for south Indian children by considering a sample from a larger geographical area with a greater sample size of 2500. Accuracy of both the models for South Indian children was analysed using root mean square error. Significant differences were observed in estimated age across all age groups and between sexes, using above models. Root mean square error using Cameriere’s Italian model was 1.54 and for Balwant Rai Indian model, it was found to be 1.78. The error for all the age groups was found to be 1.49 to 1.60 using Cameriere’s model and 1.41 to 2.1 using Rai’s model. A novel Population specific model was derived based on open apices of left mandibular seven teeth. Root mean square error with novel derived Population specific model was found to be less than 1 year overall being 0.94 and for all the age groups ranged from 0.91 to 0.96. The derived Population specific model exhibits superior accuracy compared to both the models and hence it can be considered for dental age estimation of the south Indian children in medico-legal cases.

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

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

3D CT based age estimation from the pubic symphyseal surface in an Indian population using the Chen et al. method.

The pubic symphyseal surface is one of the reliable parameters used to estimate age, as it consistently shows degenerative observational variations throughout the lifespan, particularly from the 3rd decade onwards. These changes have been extensively studied to generate population-specific models for forensic age estimation. In the past, there have been many studies used to estimate age at death from the pubis symphysis using skeletal remains. However, due to dearth of contemporary skeletal repositories, and the resource intensive maceration process required for obtaining examination quality bones, studies on cadaver are difficult to conduct. Moreover, due to recent advancements in the science of medical imaging, newer radiological modalities like computed tomography (CT) can be used to visualize previously inaccessible areas such as the pubic symphysis in cadavers and the living alike, and subsequent age estimation is feasible. Recently, Chen et al. (2008, 2011) conducted a study on the cadaveric Chinese Han population in both males and females separately by using nine morphological changes and scoring them according to the changes that occurred. The present study aimed to estimate an individual's age using CT images of the pubic symphysis, by applying the scoring method for its morphological changes given by Chen et al. The present study was conducted on 263 randomly selected participants (Males = 154, Females = 109), who came for diagnostic purposes to the hospital. The CT images of these individuals were collected after obtaining ethical approval and proper consent from the study participants. Each of the morphological indicators was assessed, and appropriate scores were given according to criteria given by Chen et al. In the present study, both linear and multiple regression models to estimate age using the pubic symphyseal morphological changes were developed. From the results of the present study, it was concluded that the Chen et al. scoring method can be used to accurately estimate age from 3DCT images of the living, and the models derived in the present study could be applicable to individuals from the Indian subcontinent.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation.

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.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

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. 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. 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. 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.

Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

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. 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. 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. 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.

Age estimation model for individual tree in natural Larix gmelinii forest based on random forest model

To accurately estimate the age of individual tree and to achieve full-cycle sustainable management of natural Larix gmelinii forest in Great Xing'an Mountains of northeastern China, we constructed individual tree age prediction model using stepwise regression and random forest algorithms based on 44 fixed plots data and 280 stan-dard tree cores obtained from the Pangu Forest Farm. We analyzed the influence of stand structure, site conditions, and competition index on the accuracy of model prediction. The model was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the random forest model had the highest prediction accuracy when number of decision trees was 1500 and number of node con-tention variables was 8. The random forest model had better accuracy and prediction ability than the stepwise regression model, with R2, RMSE and MAE of 0.5882, 9.9259 a, 8.1155 a. Diameter at breast height was the most important factor affecting age prediction (83.8%), followed by tree height (34.4%), elevation (17.9%), and basal area per hectare (17.5%). The random forest algorithm exhibited better adaptability and modeling effect on constructing a predictive model for individual tree age. This research contributed to improving the accuracy of growth and harvest estimation for L. gmelinii, and could provide a reference for other scientific studies related to tree age estimation in forests.

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

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

Epigenetic-based age prediction in blood samples: Model development.

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

OPG-based dental age estimation using a data-technical exploration of deep learning techniques.

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes.