Evergrowing incisors of diprotodont marsupials record age and life history

OBJECTIVE

Tooth growth and wear are commonly used tools for determining the age of mammals. The most speciose order of marsupials, Diprotodontia, is characterised by a pair of procumbent incisors within the lower jaw. This study examines the growth and wear of these incisors to understand their relationship with age and sex.

DESIGN

Measurements of mandibular incisor crown and root length were made for two sister species of macropodid (kangaroos and wallabies); Macropus giganteus and Macropus fuliginosus. Histological analysis examined patterns of dentine and cementum deposition within these teeth. Broader generalisability within Diprotodontia was tested using dentally reduced Tarsipes rostratus - a species disparate in body size and incisor function to the studied macropodids.

RESULTS

In the macropodid sample it is demonstrated that the hypsodont nature of these incisors makes measurements of their growth (root length) and wear (crown length) accurate indicators of age and sex. Model fitting finds that root growth proceeds according to a logarithmic function across the lifespan, while crown wear follows a pattern of exponential reduction for both macropodid species. Histological results find that secondary dentine deposition and cementum layering are further indicators of age. Incisor measurements are shown to correlate with age in the sample of T. rostratus.

CONCLUSIONS

The diprotodontian incisor is a useful tool for examining chronological age and sex, both morphologically and microstructurally. This finding has implications for population ecology, palaeontology and marsupial evolution.

Technical note: RP Atlas-an unprecedented proposal for a Brazilian population dental development chart based on the London atlas

Age estimation methods through developmental atlases are simplified by image comparison using radiographs. Atlas-based methods have been utilized to create population-specific charts addressing population diversity. To tackle the absence of a dedicated atlas for the Brazilian population, the objective of the present study was to create a new atlas for dental development in Brazil based on the London Atlas chart. The new atlas (RP Atlas) is based on the median of tooth development, eruption, and resorption in individuals aged 5 to 23. The sample (567 individuals) was divided between females (n = 290) and males (n = 277). The RP Atlas was handcrafted, scanned, colored, and clustered in two atlases divided by sex. The development of a specific atlas for the Brazilian population has the potential to enhance the accuracy of age estimation in forensic scenarios, notably in a mixed country like Brazil. Further research is needed to assess the suitability of the newly developed atlas for the Brazilian population.

The effect of birth weight on tooth development by Demirjian's method

OBJECTIVES

The aim of this study was to investigate the impact of birth weight on tooth development in children aged 7-8 years.

MATERIALS AND METHODS

This retrospective cohort study comprised 75 children born at Bint Al-Huda Hospital, Bojnurd, in 2013-2014. The children were categorized into three groups based on their birth weight: Normal Birth Weight (NBW), Low Birth Weight (LBW), and Very Low Birth Weight (VLBW). Panoramic radiographs were taken for orthodontic examination, and Demirjian's 8-teeth method was employed to determine dental age. The study compared dental and chronological age within each group. Data analysis utilized SPSS software version 26, employing One-way ANOVA and chi-square tests. Statistical significance was set at P ≤ 0.05.

RESULTS

The mean difference in dental and chronological age for Very Low Birth Weight (VLBW) children was 0.22 ± 0.44 years, for Low Birth Weight (LBW) children it was 0.19 ± 0.45 years, and for Normal Birth Weight (NBW) children, it was 0.08 ± 0.46 years. Although the mean difference decreased with increasing birth weight, this trend did not achieve statistical significance (P = 0.55). Furthermore, no significant differences were observed between the weight groups (P = 0.529) or genders (P = 0.191).

Radiological Evaluation of the Accuracy of Demirjian, Nolla, and Willems Methods for Dental Age Estimation in 3-17-Year-Old Iranian Children

Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t-test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years (P < 0.001) in girls and 0.09 years (P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years (P = 0.47) in girls and 0.12 years (P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years (P < 0.001) in girls and 0.40 years (P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.

Age estimation by volumetric analysis of teeth using cone beam computed tomography

OBJECTIVES

To evaluate the feasibility of using the pulp volume (Pv) to total volume (Tv) ratio (Pv:Tv), obtained from cone beam computed tomography (CBCT) scans of single-rooted teeth, for age estimation in a Brazilian population sample.

METHODS

After obtaining approval from the ethics committee, the study commenced by applying inclusion criteria to screen CBCT scans, resulting in a probability-based sample of participants aged 18 years and older (ranging from 18 to 82 years, with a mean age of 46.44 years). A total of 517 single-rooted teeth, including maxillary central incisors (CI), mandibular canines (C), and mandibular first premolars (FP), were chosen based on excellent agreement values (> 0.9). Pv and Tv measurements were conducted using semi-automatic segmentation with ITK-SNAP 3.8 software. Statistical analysis was performed using Jamovi software, with a significance level set at 5% (α = 0.05).

RESULTS

A strong negative correlation (r > -0.7) was observed between chronological age and the Pv:Tv ratio across all examined teeth. However, when conducting regression analysis with Pv:Tv data and chronological age as the independent variable, only the mandibular FP teeth exhibited a normal distribution. The resulting linear model demonstrated moderate predictive value (approximately 64%) in explaining the variance in chronological age, but caution should be exercised when interpreting these findings.

CONCLUSIONS

The method of measuring individual tooth volume using CBCT to estimate chronological age via Pv:Tv has been demonstrated as effective and reproducible within the Brazilian population sample.

Dental age assessment and dental scoring systems: combined different statistical methods

INTRODUCTION

Age Estimation has been considered as a human basic right, carried out through the use of tables for dental age assessment based on the chronology of tooth eruption. As such, the final aim of this investigation is to create tables with applicability to the Portuguese population, for the different scoring systems used and combined different statistical approaches.

MATERIALS AND METHODS

For this purpose, dental age assessment was achieved in all four third molars, using different scoring systems, in a total sample of 626 orthopantomograms (324 females, 302 males), aged between 12 and 25 years old, from the database population of Lisbon North University Hospital Center, approved by the Ethic Committee.

RESULTS

The values of validation showed excellent results both on precision and on reproducibility. Mostly all methods showed statistically significant differences between the estimated age and the chronological age and, therefore, the presence of estimation errors. Kullman's and Mincer's methods are the ones with best applicability in the Portuguese population, in the lower third molars. The reliability measures (sensitivity, specificity and accuracy) values decrease as age increases.

CONCLUSION

A combination of the scoring systems as a protocol for dental age assessment in Portuguese nationality was established. Tables, for all the scoring systems used, were made with applicability in the Portuguese population.

Radiological assessment of periodontal ligament space visibility on third molars for forensic age assessment - a comparison study of three different staging scales

Various staging scales have been proposed for the assessment of the visibility of the periodontal ligament space of mandibular third molars on dental panoramic radiographs (PANs) for forensic age assessment in living individuals. However, up to now, there has been no systematic comparison between these staging scales available. We directly compared the 2010 staging scale proposed by Olze et al. with the 2017 staging by Lucas et al. and the 2020 staging by Guo et al. in a German study population. We evaluated 233 PANs from 115 females and 118 males aged 20.0 to 40.9 years using three independent examiners, with one examiner conducting two assessments. We examined the correlation between age and stage, as well as the inter- and intra-rater reliabilities. While the point estimates for the correlation coefficient and the reliability measures were lowest for the Guo scale and highest for the Olze scale, confidence intervals showed a large overlap, particularly for the scales of Olze et al. and Lucas et al. The correlation coefficients between stage and age were consistently lower in females than in males across all methods. In summary, we showed that the staging scales of Olze et al. and Lucas et al. were very similar. The Olze method showed higher point estimates across all analyses, and because there are more reference data available for this method, we argue that it should be preferred as the method of choice for further studies in the field. However, Guo method could be considered for instances, in which the inter-radicular periodontal ligament is not evaluable.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

Applicability of Cameriere's third molar maturity index on orthopantomograms and computed tomography scans from a French population

Cameriere developed a method on orthopantomograms (OPG) to assess adult age of 18 years based on the relationship between age and the third molar maturity index I3M. The aim of this study was to evaluate whether Cameriere's method could be applied to computed-tomography scans (CT-scans) from a population of French juveniles and young adults and compare the results obtained from OPG of the same individuals. Our sample comprised 200 examinations that had been performed at the radiological department of a French University hospital between 2007 and 2020. Each patient had received an OPG and a cranial CT scan for medical purposes, and we used a similar adaptation of I3M based on OPG to determine the I3M based on CT scans. Due to exclusion criteria, our final sample comprised 71 OPGs and 63 CT scans. Based on the 71 OPGs, there was concordance between chronological age and estimated age, with a sensitivity of 78.57%, a specificity of 89.47%, and a misclassified rate of 18.03% based on tooth 38, and a sensitivity of 78.79%, a specificity of 91.67%, and a misclassified rate of 17.78% based on tooth 48. Our results based on CT scans presented concordance between chronological age and estimated age for tooth 38 described by a sensitivity of 77.78%, a specificity of 94.12%, and a misclassified rate of 16.98%. The concordance between chronological age and estimated age based on 48 had a sensitivity of 75.00%, a specificity of 93.75%, and a misclassified rate of 19.23%. The > 90% ICC indicate an excellent similarity between measurements of teeth 38 and 48 based on OPGs and CT scans. This study has revealed the applicability of the Cameriere's method to calculate the I3M based on CT scans from a French population. The results based on CT scans are similar to results based on OPGs from the same individuals.

Information fusion for infant age estimation from deciduous teeth using machine learning

OBJECTIVES

Over the past few years, several methods have been proposed to improve the accuracy of age estimation in infants with a focus on dental development as a reliable marker. However, traditional approaches have limitations in efficiently combining information from different teeth and features. In order to address these challenges, this article presents a study on age estimation in infants with Machine Learning (ML) techniques, using deciduous teeth.

MATERIALS AND METHODS

The involved dataset comprises 114 infant skeletons from the Granada osteological collection of identified infants, aged between 5 months of gestation and 3 years of age. The samples consist of features such as the maximum length and mineralization and alveolar stages of teeth. For the purpose of designing a method capable of combining all the information available from each individual, a Multilayer Perceptron model is proposed, one of the most popular artificial neural networks. This model has been validated using the leave-one-out experimental validation protocol. Through different groups of experiments, the study examines the informativeness of the aforementioned features, individually and in combination.

RESULTS

The results indicate that the fusion of different variables allows for more accurate age estimates (RMSE = 66 days) than when variables are analyzed separately (RMSE = 101 days). Additionally, the study demonstrates the benefits of involving multiple teeth, which significantly reduces the RMSE compared to a single tooth.

DISCUSSION

This article underlines the clear advantages of ML-based methods, emphasizing their potential to improve the accuracy and robustness when estimating the age of infants.

Dental age estimation methods applied to Indian children and adolescents: A systematic review and meta-analysis

Country-specific systematic reviews and meta-analyses have been proposed to compile the available literature and rank methods based on their performance for a target population. India is a country with a vast scientific literature on dental age estimation. This systematic review aimed to provide evidence to help the decision of experts regarding the method of choice for dental age estimation in India. The research protocol was registered in Open Science Framework. Literature Search was performed in Embase, LILACS, MedLine (via PubMed), SciELO, Scopus and Web of Science. Grey Literature was searched in Google Scholar and ProQuest. Observational cross-sectional studies that compared chronological and estimated ages using Demirjian (original [DEM] and Chaillet's modification [modified-DEM]) and Acharya (ACH) methods were included. JBI tool was used to assess the risk of bias. The search detected 9799 studies, out of which 56 were eligible (n=13,107 panoramic radiographs of Indian individuals). Low risk of bias was registered for 48 studies, while 8 presented a moderate risk of bias. The meta-analysis showed a standardized mean difference between chronological and estimated ages of -0.11 (95%CI: -0.29; 0.07), 0.74 (95%CI: 0.39; 1.09), and -0.01 (95%CI: -0.23; 0.22) years for DEM, modified-DEM and ACH, respectively. High heterogeneity (I2=88-93%) was observed across studies for all the methods, including subgroup analyses based on sex. This study ranked ACH, DEM and modified-DEM (from the best to the worse) performances in the Indian population.

Third molar eruption in dental panoramic radiographs as a feature for forensic age assessment - new reference data from a German population

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.

Skeletal maturation evaluation: which is the reliability of dental calcification Demirjian method versus hand-wrist X-ray in growing subjects? A systematic review

OBJECTIVES

This systematic review aimed at evaluating the reliability of dental maturation (DM) according to Demirjian method compared to hand and wrist maturation (HWM) to assess skeletal maturity (SM) in growing subjects, to identify the teeth and the corresponding mineralisation stages related to the pubertal growth spurt (PGS).

MATERIALS AND METHODS

PubMed, Scopus, and Web of Science were systematically searched until January 5th, 2024, to identify observational cross-sectional studies that assessed the reliability of Demirjian method compared to the HWM methods (i.e., Grave and Brown and Fishman) in growing subjects. The quality assessment was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist.

RESULTS

Out of 136 papers suitable for title/abstract screening, 19 included studies. Of them, 17 papers showed the reliability of Demirjian DM method compared to HWM Fishman and Grave and Brown methods to assess SM in growing subjects. According to JBI Critical Appraisal Checklist, 12 papers were high-quality studies and 7 papers were medium-quality studies.  Conclusions: The mandibular second molar might be considered as the best indicator compared to other teeth and that the peak of growth occurs no earlier than stage F in females and stage G in males according to Demirjian method. Also, the mandibular canine might be analysed as indicator of SM in males, and results suggest that the peak of growth occurs no earlier than maturation stage F according to Demirjian method, only in male subjects. Further studies are needed to confirm these findings.

Evaluating legal age of 18 years through observation of third molars using Gambier et al. method in an orthopantomographic sample of subadults from South India

In forensic practice, medicolegal physicians are often tasked with estimating age using dental evidence. This calls for an uncomplicated, reliable, and reproducible method for dental age estimation, enabling physicians to proceed without specific odontological expertise. Among various dental methods, third molar eruption analyses are less complicated and easier to perform. In our study, we explored the effectiveness of Gambier et al.'s scoring system, which examines the eruption of all third molars. We retrospectively analysed 1032 orthopantomograms (528 males and 504 females) of individuals aged between 15 and 24 years. The mean chronological age increased with the progression of stages (1 to 3) and phases (A to D) of the third molar eruption for both sexes. In terms of stages, none showed significant discrimination between minors (<18 years) and adults (>18 years), especially for males. However, Gambier's phase D displayed a relatively high likelihood of being 18 years or older, with an overall 85.9 % of males and 95.7 % of females having all third molars in stage 3 being 18 years or older. While the tested method could be helpful in indicating the completion of the 18th year of life, caution is advised (due to a high percentage of false positives), and it should be used alongside other age assessment methods by experts.

Individual dental and skeletal age assessment according to Demirjian and Baccetti: Updated norm values for Central-European patients

PURPOSE

Chronological age often differs from dental and skeletal age. With orthopantomograms and lateral cephalograms, dental and skeletal development can be determined according to the methods published by Demirjian et al. and Baccetti et al. However, gender and skeletal class as possible confounders were frequently not considered and available norm values are not up-to-date. This retrospective cross-sectional study thus aimed to evaluate effects of skeletal class and gender on dental and skeletal age of growing patients and to generate updated norm values for contemporary Central-European patients.

METHODS

A total of 551 patients were included in the dental and 733 in the skeletal age assessment, respectively. Dental analysis was based on tooth mineralisation stages in orthopantomograms (Demirjian) and skeletal age was defined by cervical vertebrae maturation stages (CVMS) in lateral cephalograms (Baccetti). Skeletal class was determined by the individualised ANB angle of Panagiotidis/Witt. With nonlinear regression analysis a formula for determining dental age was established. Effects of gender and skeletal class were evaluated and updated norm values generated.

RESULTS

Inter- and intrarater reliability tests revealed at least substantial measurement concordance for tooth mineralisation and CVMS. Demirjian stages and CVMS significantly depended on gender with girls developing earlier. Skeletal class significantly affected skeletal age only, but without clinical relevance. Updated norm values for dental age differed significantly from the original values of Demirjian and the values for skeletal age differed from those published by Baccetti.

CONCLUSION

Optimised norms, separated by gender, increase precision in determining individual dental and skeletal age during orthodontic treatment planning. Further studies analysing the effect of skeletal class on dental and skeletal development are needed.

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.

Considerations for age estimation accuracy: Method-derived outcomes and practitioner interpretations

Recent research observed 92% accuracy for age-at-death estimations by U.S. forensic anthropologists. The present study compares this case report level accuracy to method level accuracy for the most commonly used methods in U.S. casework, drawing from the Forensic Anthropology Database for Assessing Methods Accuracy (FADAMA). Method application rate (i.e., how often a method is used in casework) was analyzed for n = 641 cases and identified 15 methods with an application rate >45 cases, and the present study focused further analyses on these 15 methods. Of the 15, only four yielded accuracies greater than or equal to the 92% documented for case-report level accuracy. The other 11 methods produced accuracy rates ranging from 54% to 91%, with six of these below 70% This disconnect between highly accurate age estimations at the case report level compared to the poor performance at method level suggests that practitioner interpretation and synthesis of the methods' outcomes is a critical step for increasing the accuracy rates of the age estimations as reported on the final case report. This inference was further supported by the study's results which indicated that practitioner interpretations of frequently used method combinations improve accuracy and age range width of age estimation. The study also performed a Fisher's Exact test to assess whether case report-level accuracy differed with the number of aging methods used in a case, and found no significant differences.

Decision tree analysis for age estimation in living individuals: integrating cervical and dental radiographic evaluations within a South African population

Age estimation in living individuals around the age of 18 years is medico-legally important in undocumented migrant cases and in countries like South Africa where many individuals are devoid of identification documents. Establishing whether an individual is younger than 18 years largely influences the legal procedure that should be followed in dealing with an undocumented individual. The aim of this study was to combine dental third molar and anterior inferior apophysis ossification data for purposes of age estimation, by applying a decision tree analysis. A sample comprising of 871 black South African individuals (n = 446 males, 425 = females) with ages ranging between 15 and 24 years was analyzed using panoramic and cephalometric radiographs. Variables related to the left upper and lower third molars and cervical vertebral ring apophysis ossification of C2, C3, and C4 vertebrae analyzed in previous studies were combined in a multifactorial approach. The data were analyzed using a pruned decision tree function for classification. Male and female groups were handled separately as a statistically significant difference was found between the sexes in the original studies. A test sample of 30 individuals was used to determine if this approach could be used with confidence in estimating age of living individuals. The outcomes obtained from the test sample indicated a close correlation between the actual ages (in years and months) and the predicted ages (in years only), demonstrating an average age difference of 0.47 years between the corresponding values. This method showed that the application of decision tree analysis using the combination of third molar and cervical vertebral development is usable and potentially valuable in this application.

Does third molar agenesis influence the second lower molar mineralization?

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.

Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults

BACKGROUND

The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, supervised machine learning modelling -namely support vector regression (SVR) with linear and polynomial kernel, and regression tree - was tested and compared with the multiple linear regression model.

MATERIAL AND METHODS

CBCT scans from 99 patients aged between 20 to 59.99 was collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study. Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age.

RESULTS

No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel ( = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth.

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.

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.

Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning

AIM

Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning.

METHODS

This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.

RESULTS

Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.

CONCLUSION

This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

Fully automated deep learning approach to dental development assessment in panoramic radiographs

BACKGROUND

Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning.

METHODS

The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated.

RESULTS

The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method.

CONCLUSIONS

These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.