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.

The relevance of taurodontism in forensic dental age estimation.

Taurodontism is a dental morphological anomaly characterized by enlarged pulp cavities repositioned towards the apical region of the tooth, coupled with shortened root structures. Molars are commonly affected by this alteration. Certain populations exhibit up to 48% prevalences for this dental alteration, underscoring its significance in dental age estimation (DAE). In the field of DAE, an individual's chronological age is inferred from specific dental features, frequently employed within the forensic context. The effect of taurodontism on the features of DAE is an unanswered issue. The influence of taurodontism on eruption, mineralization, radiographic visibility of root canals, and radiographic visibility of the periodontal ligament space in mandibular third molars- some of the established criteria for DAE as examples-is currently not systematically examined. Some common staging scales for the dental features of DAE cannot technically be applied to taurodontic teeth. Additionally, given the association of taurodontism with syndromes affecting tooth development, caution is warranted in age assessment procedures. Notably, taurodontic teeth may serve as indicators of syndromes influencing skeletal development, further emphasizing the relevance of taurodontism in forensic age assessment. Presumably taurodontic teeth were included in reference data to some extent due to their partially high prevalence in the past, whereby the influence of taurodontism has been statistically absorbed within the overall spread of the features. Future studies should compare the temporal course of these tooth characteristics in affected and unaffected teeth. Subsequent initiatives should focus on raising awareness among forensic dentists regarding taurodontism, necessitating in-depth exploration of the subject.

Age-related patterns in mandibular third molar eruption: presenting new forensic age assessment reference data from a Chinese population.

Valid reference data are essential for reliable forensic age assessment procedures in the living, a fact that extends to the trait of mandibular third molar eruption in dental panoramic radiographs (PAN). The objective of this study was to acquire valid reference data for a northern Chinese population. The study was guided by the criteria for reference studies in age assessment.To this end, a study population from China comprising 917 panoramic radiographs obtained from 430 females and 487 males aged between 15.00 and 25.99 years was analysed. Of the 917 PANs, a total of 1230 mandibular third molars were evaluated.The PANs, retrospectively evaluated, were performed for medical indication during the period from 2016 to 2021. The assessment of mandibular third molars was conducted using the staging scale presented by Olze et al. in 2012. Two independent examiners, trained in assessing PANs for forensic age estimation, evaluated the images. In instances where the two examiners diverged in their assessments these were subsequently deliberated, and a consensus stage was assigned.The mean age increased with higher stages for both teeth and both sexes. The minimum age recorded for stage D, indicating complete tooth eruption, was 15.6 years in females and 16.1 years in males. Consequently, the completion of mandibular third molar eruption was observed in both sexes well before reaching the age of 18. In light of our results, it is evident that relying solely on the assessment of mandibular third molar eruption may not be sufficient for accurately determining the age of majority. Contrary to previous literature, this finding of a completed eruption of the mandibular third molars in northern Chinese individuals is only suitable for detecting the completion of the 16th year of life in males according to our results. However, as the results are inconsistent compared to other studies in the literature, the trait should not be used as the only decisive marker to prove this age threshold in males from northern China.

Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population.

BACKGROUND & OBJECTIVE: Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. MATERIAL & METHODS: In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. RESULTS: Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age. CONCLUSION: The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.

Sex estimation using maxillary sinus volume for Chinese subjects based on cone-beam computed tomography.

BACKGROUND: Sex estimate is a key stage in forensic science for identifying individuals. Some anatomical structures may be useful for sex estimation since they retain their integrity even after highly severe events. However, few studies are focusing on the Chinese population. Some researchers used teeth for sex estimation, but comparison with maxillary sinus were lack. As a result, the objective of this research is to develop a sex estimation formula for the northwestern Chinese population by the volume of the maxillary sinus and compare with the accuracy of sex estimation based on teeth through cone-beam computed tomography (CBCT). METHODS: CBCT images from 349 samples were used to establish and verify the formula. The volume of both the left and right maxillary sinuses was measured and examined for appropriate formula coefficients. To create the formula, we randomly picked 80% of the data as the training set and 20% of the samples as the testing set. Another set of samples, including 20 males and 20 females, were used to compare the accuracy of maxillary sinuses and teeth. RESULTS: Overall, sex estimation accuracy by volume of the left maxillary sinus can reach 78.57%, while by the volume of the right maxillary sinus can reach 74.29%. The accuracy for females, which can reach 91.43% using the left maxillary sinus, was significantly higher than that for males, which was 65.71%. The result also shows that maxillary sinus volume was higher in males. The comparison with the available results using measurements of teeth for sex estimation performed by our group showed that the accuracy of sex estimation using canines volume was higher than the one using maxillary sinus volume, the accuracies based on mesiodistal diameter of canine and first molar were the same or lower than the volume of maxillary sinus. CONCLUSIONS: The study demonstrates that measurement of maxillary sinus volume based on CBCT scans was an available and alternative method for sex estimation. And we established a method to accurately assess the sex of the northwest Chinese population. The comparison with the results of teeth measurements made the conclusion more reliable.

An Uncertainty-Aware and Sex-Prior Guided Biological Age Estimation From Orthopantomogram Images.

Bone age, as a measure of biological age (BA), plays an important role in a variety of fields, including forensics, orthodontics, sports, and immigration. Despite its significance, accurate estimation of BA remains a challenge due to the uncertainty error between BA and chronological age (CA) caused by individual diversity and the difficult integration of multiple factors, such as sex, and identified or measured anatomical structures, into the estimation process. To address problems, we propose an uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images (OPGs), named UASP-BAE, which models uncertainty errors while setting sex dimorphism as tractive features to enhance age-related specific features, aiming to improve the accuracy of BA estimation. Furthermore, considering the global relevance of the anatomic structure, such as the mandible, teeth, maxillary sinus, etc., a cross-attention module based on CNN and self-attention is proposed to mine the local texture and global semantic features of OPGs. Moreover, we design a novel age composition loss by cross-entropy, probability bias, and regression functions, aiming at evaluating BA's uncertainty errors and results to obtain an accurate and robust model. On 10703 OPGs from 5.00 to 25.00 years of age, our model had a best MAE value of 0.8005 years and higher than the comparison popular algorithms, which also demonstrates the method's potential for improved accuracy in BA estimation.

Automatic sex estimation using deep convolutional neural network based on orthopantomogram images.

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.

Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning.

Background: Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment. Methods: A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological score (HS) was constructed and validated. The correlation between immune microenvironment and HS was evaluated through extensive immunohistochemical data. Results: The overall classification accuracy of HCC tissues was 94.17%. The C-indexes of HS in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that the HS (HR= 4.05, 95% CI: 3.40-4.84) was an independent predictor for recurrence-free survival. Patients in HS high-risk group had elevated preoperative alpha-fetoprotein levels, poorer tumor differentiation and a higher proportion of microvascular invasion. The immunohistochemistry data linked the HS to local immune cell infiltration. HS was positively correlated with the expression level of peritumoral CD14+ cells (p= 0.013), and negatively with the intratumoral CD8+ cells (p< 0.001). Conclusions: The study established a novel histological score that predicted short-term and long-term recurrence for E-HCCs using deep learning, which could facilitate clinical decision making in recurrence prediction and management.

Three-dimensional evaluation of pharyngeal airway and maxillary arch in mouth and nasal breathing children with skeletal Class I and II.

OBJECTIVE: This study aimed to investigate whether the subjects with mouth breathing (MB) or nasal breathing (NB) with different sagittal skeletal patterns showed different maxillary arch and pharyngeal airway characteristics. METHODS: Cone-beam computed tomography scans from 70 children aged 10 to 12 years with sagittal skeletal Classes I and II were used to measure the pharyngeal airway, maxillary width, palatal area, and height. The independent t-test and the Mann-Whitney U test were used for the intragroup analysis of pharyngeal airway and maxillary arch parameters. RESULTS: In the Skeletal Class I group, nasopharyngeal airway volume (P < 0.01), oropharyngeal airway volume (OPV), and total pharyngeal airway volume (TPV) (all P < 0.001) were significantly greater in subjects with NB than in those with MB. Furthermore, intermolar width, maxillary width at the molars, intercanine width, maxillary width at the canines, and palatal area were significantly larger in subjects with NB than in those with MB (all P < 0.001). In the Skeletal Class II group, OPV, TPV (both P < 0.05) were significantly greater in subjects with NB than in those with MB. No significant differences in pharyngeal airway parameters in the MB group between subjects with Skeletal Class I and those with Skeletal Class II. CONCLUSION: Regardless of sagittal Skeletal Class I or II, the pharyngeal airway and maxillary arch in children with MB differ from those with NB. However, the pharyngeal airway was not significantly different between Skeletal Class I and II in children with MB.

Evaluation of the stability of the palatal rugae using the three-dimensional superimposition technique following orthodontic treatment.

OBJECTIVES: To determine the uniqueness and stability of the palatal rugae after orthodontic treatment. METHODS: Cast models of untreated subjects (n = 50) were obtained twice at intervals of 8-30 months. Cast models of patients who received non-extraction (n = 50) and extraction (n = 50) orthodontic treatment were obtained before and after treatment at intervals of 11-41 months and 14-49 months, respectively. All 300 cast models were scanned digitally. The palatal rugae were manually extracted and transformed into 3D point clouds using reverse engineering software. An iterative closest point (ICP) registration algorithm based on correntropy was applied, and the minimum point-to-point root mean square (RMS) distances were calculated to analyze the deviation of palatal rugae for scans of the same subject (intrasubject deviation [ISD]) and between different subjects (between-subject deviation [BSD]). Differences in ISD between each group and the deviation between ISD and BSD of all 150 subjects were evaluated. RESULTS: Significant differences were found in the 150 ISD and 1225 BSD in each group, as well as the 150 ISD and 11,175 BSD across all groups. The mean values of ISD in untreated, non-extraction and extraction group were 0.178, 0.229 and 0.333 mm, respectively. When the first ruga was excluded in the extraction group, the mean ISD decreased to 0.241 mm, which was not significantly different from that in the non-extraction group (p = 0.314). CONCLUSIONS: Orthodontic treatment can influence the palatal rugae, especially in cases of extraction. Furthermore, variation mainly existed in the first ruga in cases of extraction. However, palatal rugae are still unique and may be used as a supplementary tool for individual identification. CLINICAL SIGNIFICANCE: This study indicates that palatal rugae might be applied in the evaluation of orthodontic tooth movement and forensic individual identification. The registration algorithm based on correntropy provides a reliable, precise, and convenient method for palatal rugae superimposition.

With or without human interference for precise age estimation based on machine learning?

Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging.

Efficiency evaluation of maxillary molar distalization using Invisalign based on palatal rugae registration.

INTRODUCTION: This study aimed to compare the predicted maxillary molar distalization with the achieved clinical outcome using the palatal rugae area for registration and superimposition of digital models. Understanding Invisalign efficiency may assist clinicians in predicting changes, thus applying specific measures to minimize the chance of midcourse correction later. METHODS: The study sample included 38 patients with a mean age of 25.4 years, eligible for Invisalign treatment and requiring distalization of maxillary molars. Two digital models were acquired using iTero intraoral scanner (Align Technology, Santa Clara, Calif) before treatment and after maxillary first and second molar distalization. The 2 digital models were superimposed using the palatal rugae area for registration. The predicted tooth movement compared to the achieved values. One hundred forty-two maxillary molars (71 first molar and 71 second molar) were measured for distal movement, and 228 maxillary anterior teeth were evaluated for anterior anchorage loss. RESULTS: The predicted distal movement of the maxillary first molar (P <0.0001) and maxillary second molar (P <0.0001) differed significantly from the actual values. There was a statistically significant correlation between the amount of distal molar movement and the amount of anchorage loss (r = 0.3900, P <0.008) for the central incisor, and (r = 0.3595, P <0.013) for the lateral incisor. CONCLUSIONS: Invisalign can be used successfully for adult patients requiring maxillary molar distalization when a mean distalization movement of 2.6 mm was prescribed. Clinicians should be aware of the countereffect if maxillary molars are planned to move distally, especially if the patient presented initially with a large overjet, so the need to prescribe overcorrection or the use of auxiliaries can be addressed earlier.

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation. This study conducted a retrospective analysis of 2164 pelvis X-ray images of Chinese Han teenagers ranging from 11 to 21 years old. Key areas of the pelvis were detected with a U-Net segmentation network, and the findings were combined with the original X-ray image for regional augmentation. Bone age estimation was conducted with the enhanced and not enhanced pelvis X-ray images by separately using three convolutional neural networks (CNNs). The root mean square errors (RMSE) of the Inception-V3, Inception-ResNet-V2, and VGG19 convolutional neural networks were 0.93 years, 1.12 years, and 1.14 years, respectively, and the mean absolute errors (MAE) of these networks were 0.67 years, 0.77 years, and 0.88 years, respectively. For comparison, a network without segmentation was employed to conduct the estimation, and it was found that the RMSE of the three CNNs above became 1.22 years, 1.25 years, and 1.63 years, respectively, and the MAE became 0.93 years, 0.96 years, and 1.23 years. Bland-Altman plots and attention maps were also generated to provide a visual comparison. The proposed segmentation network can be used to reduce the influence of restrictions like image overlapping of organs and can thus increase the accuracy of pelvic bone age estimation.

Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression.

Given m d -dimensional responsors and n d -dimensional predictors, sparse regression finds at most k predictors for each responsor for linear approximation, 1 ≤ k ≤ d-1 . The key problem in sparse regression is subset selection, which usually suffers from high computational cost. In recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid to the nonapproximate method of subset selection, which is very necessary for many questions in data analysis. Here, we consider sparse regression from the view of correlation and propose the formula of conditional uncorrelation. Then, an efficient nonapproximate method of subset selection is proposed in which we do not need to calculate any coefficients in the regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from O([1/6]k3+(m+1)k2+mkd) to O([1/6]k3+[1/2](m+1)k2) for each candidate subset in sparse regression. Because the dimension d is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of nonapproximate subset selection. We also apply the proposed method in real scenarios of dental age assessment and sparse coding to validate the efficiency of the proposed method.

Lip morphology estimation models based on three-dimensional images in a modern adult population from China.

Lips are the main part of the lower facial soft tissue and are vital to forensic facial approximation (FFA). Facial soft tissue thickness (FSTT) and linear measurements in three dimensions are used in the quantitative analysis of lip morphology. With most FSTT analysis methods, the surface of soft tissue is unexplicit. Our study aimed to determine FSTT and explore the relationship between the hard and soft tissues of lips in different skeletal occlusions based on cone-beam CT (CBCT) and 3dMD images in a Chinese population. The FSTT of 11 landmarks in CBCT and 29 lip measurements in CBCT and 3dMD of 180 healthy Chinese individuals (90 males, 90 females) between 18 and 30 years were analyzed. The subjects were randomly divided into two groups with different skeletal occlusions distributed equally: 156 subjects in the experimental group to establish the prediction regression formulae of lip morphology and 24 subjects in the test group to assess the accuracy of the formulae. The results indicated that FSTT in the lower lip region varied among different skeletal occlusions. Furthermore, sex discrepancy was noted in the FSTT in midline landmarks and linear measurements. Measurements showing the highest correlation between soft and hard tissues were between total upper lip height and Ns-Pr (0.563 in males, 0.651 in females). The stepwise multiple regression equations were verified to be reliable with an average error of 1.246 mm. The method of combining CBCT with 3dMD provides a new perspective in predicting lip morphology and expands the database for FFA.

Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.

Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison between manual and machine learning methods based on a large sample of dental orthopantomograms (OPGs). In total, we collected 10,257 orthopantomograms for the study. We derived logistic regression linear models for each legal age threshold (14, 16, and 18 years old) for manual method and developed the end-to-end convolutional neural network (CNN) which classified the dental age directly to compare with the manual method. Both methods are based on left mandibular eight permanent teeth or the third molar separately. Our results show that compared with the manual methods (92.5%, 91.3%, and 91.8% for age thresholds of 14, 16, and 18, respectively), the end-to-end CNN models perform better (95.9%, 95.4%, and 92.3% for age thresholds of 14, 16, and 18, respectively). This work proves that CNN models can surpass humans in age classification, and the features extracted by machines may be different from that defined by human.

Three-dimensional superimposition of digital models for individual identification.

Dentition is an individualizing structure in humans that may be potentially utilized in individual identification. However, research on the use of three-dimensional (3D) digital models for personal identification is rare. This study aimed to develop a method for individual identification based on a 3D image registration algorithm and assess its feasibility in practice. Twenty-eight college students were recruited; for each subject, a dental cast and an intraoral scan were taken at different time points, and digital models were acquired. The digital models of the dental casts and intraoral scans were assumed as antemortem and postmortem dentition, respectively. Additional 72 dental casts were extracted from a hospital database as a suspect pool together with 28 antemortem models. The dentition images of all of the models were extracted. Correntropy was introduced into the traditional iterative closest point algorithm to compare each postmortem 3D dentition with 3D dentitions in the suspect pool. Point-to-point root mean square (RMS) distances were calculated, and then 28 matches and 2772 mismatches were obtained. Statistical analysis was performed using the Mann-Whitney U test, which showed significant differences in RMS between matches (0.18±0.03mm) and mismatches (1.04±0.67mm) (P<0.05). All of the RMS values of the matched models were below 0.27mm. The percentage of accurate identification reached 100% in the present study. These results indicate that this method for individual identification based on 3D superimposition of digital models is effective in personal identification.

Accuracy of the Demirjian, Willems and Nolla methods for dental age estimation in a northern Chinese population.

OBJECTIVE: The objective of the present study was to evaluate the accuracy of the Demirjian, Willems, and Nolla methods for dental age estimation on a sample of the northern Chinese population. DESIGN: The study consisted of 2000 panoramic radiographs (1000 boys and 1000 girls) with an age range between 5 and 14 years. The mean error and absolute mean error were calculated according to each method, and the accuracy was statistically analysed. RESULTS: The three methods used for Chinese subjects overestimated the dental age by 1.16, 0.50, and 0.07 years. The absolute mean error was largest in most age groups for the Demirjian method, which was considered inaccurate in age estimation for teenagers, and it was more than 1.00 years for only several age groups for the Willems method and only girls aged 14 years for the Nolla method. The mean error and absolute mean error were lowest for the Nolla method and highest for the Demirjian method. CONCLUSIONS: Although the Demirjian method is frequently used in Chinese subjects for legal and medical purposes, the Willems and Nolla methods were more reliable than the Demirjian method. Among the three methods, the accuracy in the northern Chinese subjects was highest for the Nolla method. Therefore, it is recommended to evaluate the accuracy of different methods before assessing the age in specific populations.

Three-dimensional prediction of nose morphology in Chinese young adults: a pilot study combining cone-beam computed tomography and 3dMD photogrammetry system.

The nose is the most prominent part of the face and is a crucial factor for facial esthetics as well as facial reconstruction. Although some studies have explored the features of external nose and predicted the relationships between skeletal structures and soft tissues in the nasal region, the reliability and applicability of methods used in previous studies have not been reproduced. In addition, the majority of previous studies have focused on the sagittal direction, whereas the thickness of the soft tissues was rarely analyzed in three dimensions. A few studies have explained the specific characteristics of the nose of Chinese individuals. The aim of this study was to investigate the relationship between the hard nasal structures and soft external nose in three dimensions and to predict the morphology of the nose based on hard-tissue measurements. To eliminate the influence of low resolution of CBCT and increase the accuracy of measurement, three-dimensional (3D) images captured by cone-beam computed tomography (CBCT) and 3dMD photogrammetry system were used in this study. Twenty-six measurements (15 measurements for hard tissue and 11 measurements for soft tissue) based on 5 craniometric and 5 capulometric landmarks of the nose of 120 males and 120 females were obtained. All of the subjects were randomly divided into an experimental group (180 subjects consisting of 90 males and 90 females) and a test group (60 subjects consisting of 30 males and 30 females). Correlation coefficients between hard- and soft-tissue measurements were analyzed, and regression equations were obtained based on the experimental group and served as predictors to estimate nasal morphology in the test group. Most hard- and soft-tissue measurements appeared significantly different between genders. The strongest correlation was found between basis nasi protrusion and nasospinale protrusion (0.499) in males, and nasal height and nTr-nsTr (0.593) in females. For the regression equations, the highest value of R2 was observed in the nasal bridge length in males (0.257) and nasal tip protrusion in females (0.389). The proportion of subjects with predicted errors < 10% was over 86.7% in males and 70.0% in females. Our study proved that a combined CBCT and 3dMD photogrammetry system is a reliable method for nasal morphology estimation. Further research should investigate other influencing factors such as age, skeletal types, facial proportions, or population variance in nasal morphology estimation.