Sexual dimorphism of maxillary sinuses in children and adolescents - A retrospective CT study

Midface Skeletal Sexual Dimorphism: Lessons Learned from Advanced Three-dimensional Imaging in the White Population

Background

Facial shape is significantly influenced by the underlying facial bony skeleton. Sexual dimorphisms in these structures are crucial for craniofacial, aesthetic, and gender-affirming surgery. Previous studies have examined the orbits and upper face, but less is known about the midface. This study aimed to elucidate the sexual dimorphism in the midface region, focusing on the maxilla and zygomatic bones.

Methods

A retrospective review was conducted using facial computed tomography scans from 101 White patients aged 20-79 years, using Materialise Mimics and 3-Matics for segmentation and 3D reconstruction. Measurements and statistical shape modeling of the midfacial skeleton were performed.

Results

Our results show a distinct sexual dimorphism in the midfacial skeletal structure across all age groups. Women typically had a narrower bizygomatic width by 1.5 mm (P = 0.04), a shallower maxillary depth by 1.6 mm (P < 0.01), and a midfacial vertical height that was 4 mm shorter than that of men (P = 0.018). In contrast, men exhibited a greater distance between the frontozygomatic sutures by 5.4 mm (P < 0.01), a 3-mm greater interorbitale distance (P < 0.01), and a 2.1-mm wider infraorbital foramina distance (P = 0.007). There were no significant differences in the pyriform and maxillary angles (P = 0.15 and P = 0.52, respectively).

Conclusions

Our analysis of midfacial skeletal anatomy revealed sexual dimorphism differences. Men exhibited more pronounced facial features than women, with a broader horizontal midfacial skeleton, a longer midfacial vertical height, and greater maxillary depths compared with women.

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 With Three-Dimensional Analysis of the Maxillary Sinus From Computed Tomography Images

Sex estimation from human skeletal relics is essential in creating an individual's biological profile. This study was performed to examine whether the dimensions and volume of the maxillary sinus can be used to estimate sex. The maxillary sinus's volume, length, width, and height were obtained on the paranasal computed tomography images of 232 adult subjects (116 males and 116 females). All parameters were significantly higher in males except for the right maxillary sinus length (P < 0.05). The highest precision for sex determination was the right maxillary sinus volume (68.1%). The ability of the maxillary sinus parameters to define sex was 78.6% for males and 58.6% for females. The precision of sex estimation of the maxillary sinus parameters was 68.2%. The volume and dimensions of the maxillary sinus can be useful for sex estimation in forensic sciences with a relatively close accuracy rate to reference value (close to 70%). The volume and dimensions of the maxillary sinus can be used for sex estimation together with other bony structures in the skull in cases where the entire skeleton is not available.

Sexual dimorphism in human midfacial growth patterns from newborn to 5 years old based on computed tomography

Recent studies have supported the presence and varying nature of craniofacial sexual dimorphism (SD) from the very first stages of ontogeny. But the exact patterns of between-sex differences during the first years of life remain obscure despite the importance of these data for craniofacial surgery treatment and forensic studies. Our study employs a large dataset of clinical computed tomography scans of individuals of East Slavonic descent from birth to 5 years of age (247 males and 184 females) to address the pattern of age-related between-sex differences in 22 linear measurements of the mid-face. At birth, SD of most dimensions is low, but it increases significantly during the first year of life. The level of SD of most variables fluctuates in both directions during the second year and peaks during the third and fourth years of life. During the sixth year, SD of about half of the variables markedly decreases. In adults, SD of all variables increases, but to a very different extent: from 2% to 13%. Most sexually dimorphic features of the facial skeleton begin to develop early in postnatal ontogeny and then may or may not become accentuated during puberty. Importantly, the patterns of age changes in the level of SD differ strongly between various dimensions, and so cannot be expressed by a single value for the whole face. Additionally, the level of SD for a particular variable is not ontogenetically stable during the first years of life.

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring

Background: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. Methods: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. Results: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. Conclusions: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.