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.

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.

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.

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.

A non-randomized concurrent controlled trial of myofunctional treatment in the mixed dentition children with functional mouth breathing assessed by cephalometric radiographs and study models.

OBJECTIVES: This study aimed to examine the clinical effects of myofunctional treatment on children with functional mouth breathing by cephalometric radiographs and study models. METHODS: A total of 224 children (6-10 years old; 114 males and 110 females; SNA°: 82.24 ± 1.67°; ANB°: 2.79 ± 0.80°, 28° < SN-GoGn° < 37°) formed three groups: MB-M group (mouth breathers with myofunctional treatment,n = 75); MB-N group (mouth breathers with no treatment,n = 70); NB group (nasal breathers with no treatment, n = 79). A blind evaluation of cephalometric radiographs and study models was conducted at T1(pre-study) and T2 (post-study), respectively. RESULTS: Two hundred four children (MB-M:66, MB-N:68, NB:70) completed the present study. At T1, MB-M and MB-N groups, compared to their NB counterpart, had greater anterior lower facial height(P < 0.01) and overjet(P < 0.001) but shorter overbite and maxillary canines width (P < 0.001). At T2, the MB-N group exhibited a higher ANB angle, anterior lower facial height, and overjet, but shorter overbite and maxillary canines width (P < 0.001). From T1 to T2, the anterior lower facial height increased, overbite and the maxillary canines width further decreased in the MB-N group (P < 0.001). However, in the MB-M group, the incisors were retracted, overbite increased (P < 0.001), anterior lower facial height increased insignificantly (P > 0.05), and maxillary canines width increased slightly (P < 0.05). In the NB and MB-M groups, the mandible showed a normal tendency to grow forward, whereas, in the MB-N group, the mandible showed a tendency to grow downward (P < 0.001). CONCLUSIONS: Mouth breathers demonstrated increased anterior facial height and overjet but reduced overbite and maxillary arch width, which improved significantly following myofunctional treatment. TRIAL REGISTRATION: TCTR: TCTR20220401001 . Registered 1stApril 2022-Retrospectively registered.