Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary. OBJECTIVES: The main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skeletal class diagnosis. METHODS: This quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 502 Arab patients diagnosed as Class II or III according to the Calculated_ANB. This parameter was defined as the difference between the measured ANB angle and the individualized ANB of Panagiotidis and Witt. In this observational study, we focused on the primary aim, i.e., the establishment of machine learning models for the correct classification of skeletal class II and III in a group of Arab orthodontic patients. For this purpose, various ML models and input data was tested after identifying the most relevant parameters by conducting a principal component analysis. As secondary outcomes this study compared the cephalometric parameters and analyzed their correlations between skeletal class II and III as well as between gender and age specific subgroups. RESULTS: Comparison of the two groups demonstrated significant differences between skeletal class II and class III patients. This was shown for the parameters NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle. SN-Pg angle, and ML-NSL angle in skeletal class III patients, and for S-N (mm) in skeletal class II patients. In skeletal class II and skeletal class III patients, the results showed that the Calculated_ANB correlated well with many other cephalometric parameters. With the help of the Principal Component Analysis (PCA), it was possible to explain about 71% of the variation between the first two PCs. Finally, applying the stepwise forward Machine Learning models, it could be demonstrated that the model works only with the parameters Wits appraisal and SNB angle was able to predict the allocation of patients to either skeletal class II or III with an accuracy of 0.95, compared to a value of 0.99 when all parameters were used ("general model"). CONCLUSION: There is a significant relationship between many cephalometric parameters within the different groups of gender and age. This study highlights the high accuracy and power of Wits appraisal and the SNB angle in evaluating the classification of orthodontic malocclusion.

The Complexity of Skeletal Transverse Dimension: From Diagnosis, Management, and Treatment Strategies to the Application of Collaborative Cross (CC) Mouse Model.

This study investigates the significance of skeletal transverse dimension (STD) in orthodontic therapy and its impact on occlusal relationships. The primary goal is to enhance understanding and promote the integration of transverse skeletal diagnostics into routine orthodontic assessments. To achieve this aim, the study employs a comprehensive approach, utilizing model analysis, clinical assessments, radiographic measurements, and occlusograms. The initial step involves a meticulous assessment of deficiencies in the maxilla, mainly focusing on transverse dimension issues. Various successful diagnostic methods are employed to ascertain the type and presence of these deficiencies. Furthermore, the study compares surgically assisted maxillary expansion (SARME) and orthopedic maxillary expansion (OME) in addressing skeletal transverse issues. Stability assessments and efficacy analyses are conducted to provide valuable insights into the superiority of SARME over OME. The findings reveal that proper evaluation of STD is crucial in orthodontic diagnosis, as overlooking transverse dimension issues can lead to complications such as increased masticatory muscle activity, occlusal interferences, and an elevated risk of gingival recession. Surgically assisted maxillary expansion emerges as a more stable solution than orthopedic methods. In conclusion, incorporating skeletal transverse diagnostics into routine orthodontic assessments is imperative for achieving optimal occlusal relationships and minimizing negative consequences on dentition, periodontium, and joints. The study emphasizes the significance of accurate three-dimensional assessments and recommends the consideration of SARME over OME for addressing skeletal transverse deficiencies. Finally, the Collaborative Cross (CC) mouse model is also a novel mouse model for studying complex traits. Exploring the Collaborative Cross mouse model opens avenues for future research, promising further insights into transverse skeletal issues in orthodontics.

Narrating the Genetic Landscape of Human Class I Occlusion: A Perspective-Infused Review.

This review examines a prevalent condition with multifaceted etiology encompassing genetic, environmental, and oral behavioral factors. It stands as a significant ailment impacting oral functionality, aesthetics, and quality of life. Longitudinal studies indicate that malocclusion in primary dentition may progress to permanent malocclusion. Recognizing and managing malocclusion in primary dentition is gaining prominence. The World Health Organization ranks malocclusions as the third most widespread oral health issue globally. Angle's classification system is widely used to categorize malocclusions, with Class I occlusion considered the norm. However, its prevalence varies across populations due to genetic and examination disparities. Genetic factors, including variants in genes like MSX1, PAX9, and AXIN2, have been associated with an increased risk of Class I occlusion. This review aims to provide a comprehensive overview of clinical strategies for managing Class I occlusion and consolidate genetic insights from both human and murine populations. Additionally, genomic relationships among craniofacial genes will be assessed in individuals with Class I occlusion, along with a murine model, shedding light on phenotype-genotype associations of clinical relevance. The prevalence of Class I occlusion, its impact, and treatment approaches will be discussed, emphasizing the importance of early intervention. Additionally, the role of RNA alterations in skeletal Class I occlusion will be explored, focusing on variations in expression or structure that influence craniofacial development. Mouse models will be highlighted as crucial tools for investigating mandible size and prognathism and conducting QTL analysis to gain deeper genetic insights. This review amalgamates cellular, molecular, and clinical trait data to unravel correlations between malocclusion and Class I phenotypes.