Estimation of Age Using Alveolar Bone Loss: Forensic and Anthropological Applications

Age estimation from alveolar bone loss, re-evaluation of Ruquet's method

There are many dental age estimation methods, but all the methods do not correspond, especially for aging methods for adults and mature individuals, to the reality of the forensic field, which favors simple, effective, and easy-to-use methods. Ruquet (2015) developed a method based on alveolar bone loss that predicts age for individuals between 25 and 60 years old and is even more accurate for those 25-40 years old. This study re-evaluated Ruquet's alveolar bone loss method using three-dimensional imaging of individuals whose age and sex were known, without taking into account their medical conditions. Digital measurements, from the cemento-enamel junction (CEJ) to the alveolar bone crest (ABC), were performed on the mesial and distal surfaces of teeth on 243 patients, independent of the tridimensional imaging test. With these measurements, two alveolar bone loss averages (ABL) were calculated, one with all the teeth present on the arches and another with only Ramfjörd's teeth. Bone loss showed a significant correlation with age (p < 0.001). The age estimation with all teeth and with only Ramfjörd's teeth showed a statistically significant difference, and age estimation was more accurate when all teeth were used. The assessment of alveolar resorption appears to be an interesting tool for age estimation in adult individuals. However, the method still lacks precision, and the mean absolute errors (MAEs) obtained by age group were all greater than 5 years, except for the age group 35-39 years old, for the age estimation with all teeth. Further studies should explore this existing correlation between alveolar bone loss and age and refine this method to make it more accurate.

Comparison of mandibular buccal shelf morphology between adolescents and adults with different vertical patterns using CBCT

OBJECTIVE

This retrospective study aimed to analyze the anatomical structure of the mandibular buccal shelf (MBS) in adolescents and adults with different vertical patterns to determine the optimal location for miniscrew insertion in orthodontic treatment.

METHODS

Cone-beam computed tomography (CBCT) scans of 230 patients were utilized for measurements. The morphology and thickness of alveolar bone at the MBS were measured. Two-way ANOVA and regression analysis were conducted to analyze the influencing factors on alveolar bone and cortical bone thickness.

RESULTS

Age had a significant effect on alveolar bone thickness (level I: F = 62.449, level II: F = 18.86, p < 0.001), cortical bone thickness (level II: F = 18.86, p < 0.001), alveolar bone tilt (F = 6.267, p = 0.013), and second molar tilt (F = 6.693, p = 0.01). Different vertical patterns also influenced alveolar bone thickness (level I: F = 20.950, level II: F = 28.470, p < 0.001), cortical bone thickness (level I: F = 23.911, level II: F = 23.370, p < 0.001), and alveolar bone tilt (F = 27.046, p < 0.001). As age increased, the alveolar bone thickness at level I decreased by 0.096 mm and at level II decreased by 0.073 mm. Conversely, the thickness of alveolar bone at level I and level II increased by 0.06 mm and 0.075 mm, respectively. The cortical bone thickness at level I and level II increased by 0.024 mm and 0.29 mm, respectively. However, the alveolar bone thickness decreased by 0.931 mm and 1.545 mm at level I and level II, and the cortical bone thickness decreased by 0.542 mm and 0.640 mm at level I and level II, respectively.

CONCLUSION

Age, different vertical patterns, alveolar bone inclination, and different shapes of MBS significantly affected the thickness of alveolar bone and cortical bone in the MBS area. Notably, only alveolar bone thickness and cortical bone thickness at level II were affected by age and different vertical patterns simultaneously. These findings can provide valuable insights for orthodontic practitioners in selecting the most suitable location for miniscrew insertion during treatment planning.

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R² ranged from 0.92 to 0.96.

Age-group determination of living individuals using first molar images based on artificial intelligence

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.