BACKGROUND: Underlying disorders of the maxillary sinus (MS), including a history of sinus surgeries, chronic sinusitis, or congenital anomalies can potentially impact sinus function and structure, necessitating careful evaluation and management. Moreover, intact sinuses are crucial in gender determination in forensic anthropology. The present study was undertaken to check the accuracy and reliability of MS in gender determination using morphometric parameters.
METHODS: This retrospective study was carried out on 74 lateral cephalograms (37 males and 37 females) aged between 18 to 50 years from the North Indian population. The MS area was measured using a NewTom CBCT machine (NewTom, Imola, Italy) with slicer software. The anatomical landmarks for the sinus were identified, and the area was calculated in square millimeters (mm2).
RESULTS: In terms of surface area, females had a mean of 13,210.40 mm2 with a standard error of 713.46. Males, however, exhibited a higher mean surface area of 18,713.82 mm2, but with a significantly larger standard error of 3,371.70. The difference in MS area between males and females was statistically significant (p<0.01). In the receiver operating characteristic (ROC) curve, the area under the curve (AUC) was 0.77, suggesting good discriminative ability.
CONCLUSIONS: The MS area on lateral cephalograms shows significant sexual dimorphism. Overall, the findings suggest that the MS surface area can be a useful anatomical feature for distinguishing between male and female North Indian subjects, given the statistically significant difference and the good discriminative performance indicated by the ROC curve analysis.

OBJECTIVE: To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram.
METHODS: A total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods.
RESULTS: The validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found.
CONCLUSIONS: Automated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.

Background: Cephalometric studies indicate that craniofacial morphology in patients with cleft palate only (CPO) differs from other forms of orofacial clefts and healthy patients. Planning orthodontic treatment for patients with different craniofacial deformities requires knowledge on the craniofacial complex. The aim of the present study was to describe the cephalometric craniofacial morphology in adolescents with cleft palate only compared to generally healthy orthodontic patients. Methods: The study comprised 100 lateral cephalograms (taken in the years 2003-2020) of Polish patients with cleft palate only aged from 11.1 to 14.2 (mean age 12.43 y) and a matched control group of 100 children without orofacial clefts aged 12-14 (mean age 12.25). All digital images were analyzed in specialized cephalometric software. Results: Statistically significantly lower values of both SNA (p < 0.001) and ANB (p < 0.001) were found in the study group versus the control group. Mandibular line to cranial base angle (ML-NSL) as well as maxillary base to cranial base (NL-NSL) were significantly higher in the CPO group. Both the maxilla and mandible were rotated distally in CPO. Moreover, the intermaxillary vertical angle (ML-NL) was reduced in CPO. Mandibular angle in CPO was significantly higher (p = 0.005), reflecting posterior mandibular rotation. Conclusions: In adolescents with CPO, maxillary deficiency is found, without a severe sagittal jaw discrepancy, with a slight compensatory lingual inclination of the lower incisors. Mandibular deficiency in CPO is concurrent with posterior rotation and an increased mandibular angle.

BACKGROUND: Forensic dentistry integrates interdisciplinary scientific knowledge to produce accurate and reliable forensic statements. Anthropometry, essential since its introduction by Alphonse Bertillon in 1882, describes human body shapes and has significant forensic applications. This study focuses on sexual dimorphism, phenotypic differences between males and females, using lateral cephalometric measurements to determine sex in children aged 10-12 years from the Chengalpattu population.
METHODS: The cross-sectional study included 80 participants (40 boys and 40 girls). Lateral cephalograms were analyzed using Ceph Ninja Pro software to obtain 15 cephalometric measurements. Statistical analysis using SPSS Software (Version 22.0) involved t-tests to identify significant differences (P<0.05). Discriminant function analysis assessed the predictive power of these variables.
RESULTS: Seven variables showed significant differences between sexes. Discriminant models based on these variables determined sex with varying reliability. Ramus length was the most reliable predictor (81%), while maxillary length had the lowest reliability (62%).
CONCLUSIONS: The study\'s findings align with existing literature, indicating the robustness of ramus length for sex determination. However, the low reliability of maxillary length contrasts with studies that found it useful for sex differentiation, suggesting variability across populations and age groups. Combining multiple cephalometric variables improved accuracy, consistent with previous research.
CONCLUSIONS: Lateral cephalograms are effective for assessing sexual dimorphism in children. The study supports the forensic and clinical utility of cephalometric measurements and calls for further research with diverse populations and advanced imaging techniques to enhance method reliability and applicability.

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

OBJECTIVE: We conducted a three-dimensional (3D) analysis of orthodontic tooth movement (TM) using digital dental models (DMs), focusing on the palatal region of interest (PROI), aiming to evaluate the long-term stability of the PROI, validate the 3D TM analysis with PROI registration, and compare it with conventional lateral cephalometric analyses.
METHODS: Twenty adult patients treated with a multibracket appliance were evaluated at their first visit (T0) and at least 5 years later (T1) using DMs and lateral cephalograms (LCs). The long-term stability of PROI was assessed by calculating the point cloud distances between DM-T0 and DM-T1. TM analysis using DM with PROI registration for the maxillary central incisors was assessed through linear and angular measurements in the sagittal view and subsequently compared with the LCs.
RESULTS: The average point cloud distance of the PROI between DM-T0 and DM-T1 was 0.21 mm (standard deviation, 0.13 mm). TM analysis using DMs demonstrated excellent reproducibility for both linear and angular measurements (intra-rater correlation coefficient, > 0.99). The 95 % limits of agreement between the DM and LC measurements were < 5.14° for angular change, 3.53 mm for horizontal displacement, and 0.98 mm for vertical displacement. No significant differences were observed in the angular and linear measurements when the TM was compared using the DMs and LCs.
CONCLUSIONS: The PROI remained stable for over 5 years, supporting the reproducibility and accuracy of TM assessment using PROI registration in orthodontic clinical practice.
CONCLUSIONS: DM analysis lacks the risks associated with X-ray exposure and can be easily performed in daily clinical practice, indicating its potential for future clinical applications. These findings further support the use of DM with PROI registration for TM analysis in orthodontic clinical practice, emphasizing its long-term stability and reproducibility.

This study investigated compensation in skeletal Class III subjects to compare various severities of abnormal jaws. A retrospective analysis of 137 skeletal Class III cephalograms (63 males and 74 females) was conducted, with cephalometric assessments determining skeletal and dental values. The results were compared with Class I cephalograms. Incisor compensation was examined by pairing normal jaws with varied abnormal jaws, classified by severity using one standard deviation (SD). Statistical analyses included Kruskal-Wallis tests, Bonferroni tests, Spearman\'s correlations, and multiple linear regression. Four skeletal Class III groups were identified: OMx+PMd, RMx+OMd, OMx+OMd, and PMx+PMd (P = prognathic; O = orthognathic; R = retrognathic; Mx = maxilla; Md = mandible.). The upper central incisor (U1) showed proclination, and the lower central incisor (L1) showed retroclination across all groups except for U1 in PMx+PMd and L1 in OMx+OMd, which exhibited normal inclination. U1 exhibited limited compensation even with progressive maxillary retrognathism, while L1 showed limited compensation after one SD of mandibular prognathism. Maxilla (SNA) and jaw discrepancy (ANB) were inversely related to the U1 degree, whereas only jaw discrepancy (ANB) was positively related to the L1 degree. U1 in PMx+PMd and L1 in OMx+OMd showed no incisor compensation. U1 had limited compensation even with progressive maxillary retrognathism while L1 showed limited compensation after one SD mandibular prognathism.

OBJECTIVE: The present study was designed to define a novel algorithm capable of predicting female adolescents\' cervical vertebrae maturation stage with high recall and accuracy.
METHODS: A total of 560 female cephalograms were collected, and cephalograms with unclear vertebral shapes and deformed scales were removed. 480 films from female adolescents (mean age: 11.5 years; age range: 6-19 years) were used for the model development phase, and 80 subjects were randomly and stratified allocated to the validation cohort to further assess the model\'s performance. Derived significant predictive parameters from 15 anatomic points and 25 quantitative parameters of the second to fourth cervical vertebrae (C2-C4) to establish the ordinary logistic regression model. Evaluation metrics including precision, recall, and F1 score are employed to assess the efficacy of the models in each identified cervical vertebrae maturation stage (iCS). In cases of confusion and mispredictions, the model underwent modification to improve consistency.
RESULTS: Four significant parameters, including chronological age, the ratio of D3 to AH3 (D3:AH3), anterosuperior angle of C4 (@4), and distance between C3lp and C4up (C3lp-C4up) were administered into the ordinary regression model. The primary predicting model that implements the novel algorithm was built and the performance evaluation with all stages of 93.96% for accuracy, 93.98% for precision, 93.98% for recall, and 93.95% for F1-score were obtained. Despite the hybrid logistic-based model achieving high accuracy, the unsatisfactory performance of stage estimation was noticed for iCS3 in the primary cohort (89.17%) and validation cohort (85.00%). Through bivariate logistic regression analysis, the posterior height of C4 (PH4) was further selected in the iCS3 to establish a corrected model, thus the evaluation metrics were upgraded to 95.83% and 90.00%, respectively.
CONCLUSIONS: An unbiased and objective assessment of the cervical vertebrae maturation (CVM) method can function as a decision-support tool, assisting in the evaluation of the optimal timing for treatment in growing adults. Our novel proposed logistic model yielded individual formulas for each specific CVM stage and attained exceptional performance, indicating the capability to function as a benchmark for maturity evaluation in clinical craniofacial orthopedics for Chinese female adolescents.

In the past few years, there has been an enormous increase in the application of artificial intelligence and its adoption in multiple fields, including healthcare. Forensic medicine and forensic odontology have tremendous scope for development using AI. In cases of severe burns, complete loss of tissue, complete or partial loss of bony structure, decayed bodies, mass disaster victim identification, etc., there is a need for prompt identification of the bony remains. The mandible, is the strongest bone of the facial region, is highly resistant to undue mechanical, chemical or physical impacts and has been widely used in many studies to determine age and sexual dimorphism. Radiographic estimation of the jaw bone for age and sex is more workable since it is simple and can be applied equally to both dead and living cases to aid in the identification process. Hence, this systematic review is focused on various AI tools for age and sex determination in maxillofacial radiographs. The data was obtained through searching for the articles across various search engines, published from January 2013 to March 2023. QUADAS 2 was used for qualitative synthesis, followed by a Cochrane diagnostic test accuracy review for the risk of bias analysis of the included studies. The results of the studies are highly optimistic. The accuracy and precision obtained are comparable to those of a human examiner. These models, when designed with the right kind of data, can be of tremendous use in medico legal scenarios and disaster victim identification.

BACKGROUND: When it comes to orthodontic diagnosis and treatment planning, the structures of the upper and lower airway space are crucial because of the role they play in craniofacial development.
OBJECTIVE: The major objective of this study was to evaluate the accuracy of lateral cephalogram in the evaluation of upper and lower pharyngeal space by comparing it to clinical usage of cone-beam computed tomography (CBCT) in quantifying the 3D morphology of the pharyngeal airway.
METHODS: In total, 70 patients were included in the study. They had both a CBCT scan and a lateral cephalogram performed within a week of each other. Different cephalometric landmarks have been utilized to estimate linear and area dimensions for use in lateral cephalogram airway investigations. By superimposing the lateral cephalogram measurement of the vertical height of the pharyngeal airway over axial CBCT slices of 0.8 to 1 mm in thickness, airway volumes were calculated. For this study, we measured the pharyngeal airway space in each patient in two dimensions (2D) using the airway area from the lateral cephalogram and in three dimensions (3D) using the airway volume from the CBCT scan over the same region of interest, using a uniform scale and magnification throughout all split 3D volumes.
RESULTS:  The mean value of the area of pharyngeal space calculated by lateral cephalograph analysis (LCA) was 336.35 ± 86.49 mm2. The maximum value was 551.234 mm2. The minimum value was 206.32 mm2. The mean value of the volume of the same area calculated using CBCT was 3409.11 ± 1237.96 mm3. The maximum value was 5887.23 mm3. When the area calculated using LCA was compared with the volume calculated using CBCT, the correlation between them was significant statistically (r=0.831, p-value =0.000). The mean values of volume evaluated in 3D CBCT in males were 4198±1008 mm3 while for females it was 2980±1134.5 mm3. During the statistical analysis, these observations were found to have a positive correlation with increased volume of pharyngeal space in males as compared to that of females (p=0.006). The values of the area of pharyngeal space calculated using LCA in males was 370.1±60.9 mm2. while it was 301.9±88 mm2  in females.
CONCLUSIONS: The area estimated for the pharyngeal airway on LCA correlates strongly with the volume determined by a CBCT scan. Since we have considered pharyngeal space analysis using CBCT to be a reliable and standard methodology, therefore a positive correlation of area calculated using LCA with volume calculated using CBCT shows that the analysis made by LCA can be reliable.