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Estimating the age of adult human skeletons through a combination of multiple age-of-transition curves is facilitated through the development of a computer program, TA3-V1.0. Curves were generated for 47 skeletal structures and 69 transitions from one stage to the next. Data were derived from 1638 modern known-age skeletons from four continents, a diverse sample designed to accommodate human variation in genetic origin, cultural affiliation, and activity patterns. The TA3-V1.0 program generates point estimates of age (maximum likelihood estimates) and confidence intervals (80, 90, and 95%). Researchers often encounter incomplete remains, so individualized estimates are tailored to the anatomical structures that are present as well as their appearance. Several built-in prior distributions accommodate specific forensic and archaeological needs. The TA3-V1.0 program permits the estimation of age throughout adulthood. With an old-age adjustment, the correlation between true (reported) and estimated ages using skeletons from this sample is 0.92; without the correction, it is 0.91. •Age throughout adulthood is estimated from human skeletal remains.•Maximum likelihood point estimates and age intervals are produced.•Age estimates are tailored to the combination of skeletal structures and stages present.•Built-in prior distributions meet specific investigator needs.•Point estimates and age intervals correspond closely to true ages.

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.

This article is a retrospective analysis of postmortem computed tomography scans of ossification stages of the anterior and posterior intra-occipital sutures, the anterior arch of the atlas, and the neurocentral junction of the axis. We also analyzed the development of secondary ossification centers in the proximal humeral, femoral, and tibial epiphyses, and the distal femoral and tibial epiphyses. Additionally, the development of primary ossification centers in the wrist and metacarpals, and maxillary and mandibular deciduous tooth maturation. A total of 58 cadavers (35 males, 23 females), whose age ranged from 3rd month of pregnancy to 14 years, were analyzed. The results of this study show that analysis of synchondrosis closure, primary, and secondary ossification center development and deciduous tooth changes are a good tool for age estimation in subadults group (fetuses, newborns, infants, and children). The results of the study in a Polish population are consistent with those reported by other authors.

This systematic review and meta-analysis aimed to evaluate whether impaction affects the mineralization and developmental timing of mandibular third molars. A systematic literature review was conducted, adhering in part to the PRISMA statement and registered in The International Prospective Register of Systematic Reviews, PROSPERO: CRD42023454534. Four scientific databases (PubMed/Medline, Scopus, Embase, and Web of Science) were used as primary search sources, and one (OATD) was consulted to retrieve part of the grey literature. Methodological quality was appraised using the Joanna Briggs Institute (JBI) critical appraisal tool for cross-sectional studies. Mean differences in developmental timing were pooled for random-effect meta-analysis, and subgroup analyses were conducted by developmental stage and sex. Fifteen studies were included in the qualitative review, and five were eligible for meta-analysis. Impacted third molars exhibited a statistically significant developmental delay compared to non-impacted counterparts, with a pooled mean difference of 0.8 years (95 % CI: 0.61–0.98; I²= 81.5 %). Methodological quality was moderate to high, though substantial heterogeneity and population variability were observed. The results suggested that impaction can be associated with a delay of third molar development. This delay was more pronounced in females compared to males, together with variations in stage-specific sub-group analysis. •Impaction delays mandibular third molar development Vs. Non-impacted teeth.•Meta-analysis pooled delay: 0.72 years (95 % CI 0.49–0.95; I2 81.5 %).•No overall sex difference; stage-specific patterns differed by sex.•Accounting for impaction can improve fairness near the 18-year legal threshold.

Estimating the chronological age of humans is a prevalent need in forensic practice. Comparing the accuracy of different age identification methods helps provide the most reliable method for a specific population. This study aimed to compare the accuracy of age assessments using skeletal maturation and dental mineralization in a sample of Egyptians and to assess if combining both methods yields more precise age prediction. A cross-sectional study included 176 Egyptian children and adolescents aged between 8 and 16 who underwent orthopantomograms and hand-wrist radiographs. All radiographs were scored independently for skeletal maturation using the Fishman method and dental development using the Nolla method. While Fishman and Nolla methods were valid and reliable age predictors with variable sex and age group accuracy in Egyptians, the Nolla method showed superior performance. The Nolla method slightly underestimated the chronological age, while the Fishman method slightly over-estimated it, showing median differences of -0.21 and 0.17, respectively. Correlating the estimated age using the Fishman and Nolla methods and the chronological age showed intraclass correlation coefficients of (0.854 and 0.660) and (0.973 and 0.977) for females and males, respectively ( P  < 0.001). The model adopting the Nolla score exhibited the highest R² (0.973 and 0.968) and the lowest Akaike information criteria (96 and 106) for females and males, respectively, which is comparable to the model adopting both Nolla scores and skeletal maturation indicators. Therefore, we recommend adopting the model incorporating only the Nolla scores for age estimation without the need for further hand and wrist radiography.

As a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included “robust CpGs” prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for “robust” CpG markers—which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

International protocols for age estimation in subadults recommend combining different evidence according to tooth and bone maturity by radiographic examination to improve the final assessment. Scant literature could be found that observe, compare, and combine dentition and wrist bones maturation in the same sample of minors. This research aims at developing and validating an Artificial Intelligence (AI)-assisted method combining the skeletal and dental methods for age estimation in children and adolescents. The sample consisted of orthopantomography and wrist radiographs of 453 Italian subadults (227 males and 226 females) taken for clinical reasons. The age of the sample group is between 6 and 20 years old. The dental age was estimated by applying Demirjian 7-teeth, Demirjian 8-teeth, and Willems’ methods, and the skeletal age by applying Tanner Whitehouse-3-RUS (TW3-RUS) and Greulich & Pyle methods. Two machine learning models, Random Forest and Boosted, were created and trained on 70 % of all age estimates and then tested on the remaining 30 %. The results obtained by the AI for the test sample were compared to the performance of each original method. The model built using Boosted machine learning for estimated age performed better than Random Forest, with a mean prediction range of 1087 days (±1.48 years), including 95 % of the estimated sample. This error is smaller than that of the traditional methods based only on tooth mineralization or wrist bone maturation. Application of the AI-assisted approach to a sample of wrist-hand and dental radiographs taken on the same date from the same subject demonstrates that combining multiple age estimates based on skeletal and dental methods improves the accuracy and reliability of the final age assessment. ●Age assessment based on radiological evaluation is among the most accurate methods.●Dual estimation of dental and bone age is recommended in subadults.●Limited literature combines dentition and wrist bones maturation in the same sample.●AI systems can integrate estimates from both wrist-hand and dental radiographs.●Combining skeletal and dental methods improves the final age estimation accuracy.

Different studies have established that the mineralization stages of the second mandibular molar can be used in forensic age estimation. Nowadays, the estimate’s accuracy is an ethical concern, producing as few false positives (individuals incorrectly classified as older than a determined threshold) and false negatives (individuals incorrectly classified as younger than a determined threshold) as possible. Some have hypothesized that changes in teeth number may influence tooth mineralization, altering the age estimate process. This paper analyzes whether third molar agenesis affects the second mandibular molar mineralization time frame. To do so, 355 orthopantomograms were evaluated for third molar agenesis, and the second mandibular molar mineralization stage was assessed using the Demirjian stages. Student’s t -test was used to compare the difference in the mean age at which the various stages of 37 mineralization were reached in the groups with and without third molar agenesis. The level of statistical significance was set at 5%. The results pointed to a delay in second mandibular molar mineralization in the case of agenesis, suggesting the need to consider this when estimating age using dental techniques.

Forensic age estimation is a challenging field in forensic sciences because of the increase of migratory flows. Medicolegal age assessment is a key point because it has many implications for authorities. Dental age estimation is an essential part of the global age assessment. The aim of this study was to evaluate and test the accuracy of Cameriere’s cutoff values of the third molar maturity index (I3M) in assessing legal adult age of 18 years in a French population. The sample was constituted of 431 orthopantomograms performed between January 2014 and August 2017 on patients aged between 14 and 22 years. The reproducibility and repeatability of the method were high. Age distribution gradually decreases as I3M increases in both sexes. 0.08 seemed to be the best I3M cutoff. For females, the sensitivity and specificity of the test were 74.51% and 88.23%, respectively. The sensitivity and specificity for males were 92.19% and 88.35%, respectively. The accuracies were 80.74% for female, 90.57% for male. Estimated post-test probabilities were 0.879 for female and 0.899 for male. To conclude, the specific cutoff value of I3M ˂ 0.08 may be a useful additional tool in discriminating adults and minors in French population.

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