Comparison of the Cameriere's third molar maturity index and Olze et al.'s stages of radiographic visibility of the root pulp in a Turkish population.

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.

Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions.

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100 × ) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.

Intrapuparial stage aging and PMI estimation based on the developmental transcriptomes of forensically important Aldrichina grahami (Diptera: Calliphoridae) gene expression.

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.

Forensic age estimation of adolescents using computed tomography of the clavicles.

In forensic age estimation, CT imaging of the clavicles is used to determine an age over completed 21 years. If ossification of the medial clavicular epiphysis is complete, young men are assumed to be over 21 years of age. The aim of this study is to check the statistical parameters (specificity, predictive probability) for the characteristic "completed ossification of the medial clavicles". 285 male patients who, for various reasons, received a chest CT at the Medical Center of the University of Freiburg between 1st December 2019 and 6th December 2022 were screened for the study, of whom 203 patients were included in the study. The stage of clavicular ossification was classified as stage 1 - 5 according to Schmeling. While 70 out of 71 patients under 21 years of age were correctly estimated to be under 21 years of age, there was one patient whose ossification on one side was classified as stage 4 and who would therefore have been estimated to be over 21 years of age. If only subjects whose ossification stage was the same on both sides are included, the specificity of the test method is 100% and the positive predictive probability is 100%. If patients for whom only one side is stage 4 are also included, the specificity is 98.6%. Thus, only the complete and symmetrical ossification of both clavicles (stage 4 according to the Schmeling classification) in a standardised thin-layer CT can be classified as a reliable indicator of an age over 21 years in young men. In the case of asymmetric ossification of the medial clavicles (stage 4 is not reached on one side), false positive evaluations and the incorrect assumption of an age over 21 years can occur.

Forensic age estimation by MRI of the knee - comparison of two classifications for ossification stages in a German population.

AIM AND OBJECTIVES: In forensic age estimation e.g. for judicial proceedings surpassed age thresholds can be legally relevant. To examine age related differences in skeletal development the recommendations by the Study Group on Forensic Age Diagnostics (AGFAD) are based on ionizing radiation (among others orthopantomograms, plain x-rays of the hand). Vieth et al. and Ottow et al. proposed MRI-classifications for the epiphyseal-diaphyseal fusion of the knee joint to define different age groups in healthy volunteers. The aim of the present study was to directly compare these two classifications in a large German patient population. MATERIALS AND METHODS: MRI of the knee joint of 900 patients (405 female, 495 male) from 10 to 28 years of age were retrospectively analyzed. Acquired T1-weighted turbo spin-echo sequence (TSE) and T2-weighted sequence with fat suppression by turbo inversion recovery magnitude (TIRM) were analyzed for the two classifications. The different bony fusion stages of the two classifications were determined and the corresponding chronological ages assigned. Differences between the sexes were analyzed. Intra- and inter-observer agreements were determined using Cohen's kappa. RESULTS: With the classification of Ottow et al. it was possible to determine completion of the 18th and 21st year of life in both sexes. With the classification of Vieth et al. completion of the 18th year of life for female patients and the 14th and 21st year of life in both sexes could be determined. The intra- and inter-observer agreement levels were very good (κ > 0.82). CONCLUSION: In the large German patient cohort of this study it was possible to determine the 18th year of life with for both sexes with the classification of Ottow et al. and for female patients with the classification of Vieth et al. It was also possible to determine the 21st year of life for all bones with the classification of Ottow et al. and for the distal femur with the classification of Vieth et al.

Research Progress on Dental Age Estimation Based on MRI Technology.

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.

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

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.

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

OBJECTIVES: 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. CONCLUSIONS: 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.

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

OBJECTIVES: 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. CONCLUSIONS: 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.

Adults Ischium Age Estimation Based on Deep Learning and 3D CT Reconstruction.

OBJECTIVES: 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. CONCLUSIONS: 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.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: 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. CONCLUSIONS: 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.

Determining Whether an Individual is 18 Years or Older Based on the Third Molar Root Pulp Visibility in East China.

OBJECTIVES: 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. CONCLUSIONS: 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.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Population differences in dental cementum growth rates: Implications for using cementum thickness as a method for age estimation.

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

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.

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.

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.

Reliability of Radiovisiography and Cytomorphometry in Determining the Age of an Individual: A Clinical Comparative Study.

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.

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.

The utility of sternum in creating a biological profile: A review and future directions.

This review delves into the forensic utility of the sternum in creating a biological profile, focusing on sex, stature, and age estimation. Emphasizing the sternum's significance in challenging scenarios, the study supports the combined length of the manubrium and sternal body as a crucial indicator in sex and stature estimation. However, it highlights the need for caution in applying findings across diverse populations and questions the reliability of Hyrtl's law. Age estimation, primarily based on morphological changes and ossification ages, is explored, with one study showing promise but requiring further validation. While acknowledging the sternum's advantages, the review underscores potential limitations and the absence of specific studies on ancestry estimation, leaving this aspect open for future research. In conclusion, the review provides a comprehensive overview of the sternum's forensic applications, urging continued research to enhance accuracy and applicability.

Comparative evaluation of two different dental age estimation methods using tooth pulp: A CBCT study.

AIM: To estimate the age of an individual based on 3D radiographic evaluation of pulp width of maxillary central incisor. MATERIALS AND METHODS: This study included 185 CBCT images of individuals within age range of 14-64 years. Images were evaluated for maxillary central incisors and pulp width measurements were taken from cementoenamel junction and middle third of root. Obtained data was subjected to correlation and regression analysis from which the age of an individual was predicted. Results of the present study were compared with another study by the same authors. RESULTS: A negative linear relationship was obtained between age and pulp width. The standard error of estimate (SEE) in sagittal section was 11.36 years and that in coronal section was 11.23 years. The coefficient of determination for sagittal section was 0.107 and for coronal section was 0.127. An obtained regression formula was highly significant. Division of samples into various age groups reduced SEE drastically. CONCLUSION: It can be concluded that pulp width of maxillary central incisor is a reliable indicator of age estimation.