Lateral Cephalometric Parameters Variations and Machine Learning Models Among Skeletal Class II &amp; III Malocclusion of Arab Orthodontic Patients
Authors: Midlej Kareem , Watted Nezar , Awadi Obaida , Masarwa Samir , Lone Iqbal M. , Zohud Osayd , Paddenberg Eva , Krohn Sebastian , Kuchler Erika , Proff Peter , Iraqi Fuad A. 
Published: April 2024

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 objective of this research is to reveal novel knowledge concerning the cephalometric parameters among Arab patients, who are citizens of Israel, which are crucial for skeletal deformities classes II and III diagnosis. We compared the differences between the subgroups of gender (male and female) and age for each cephalometric parameter. Furthermore, we examined the correlation between these parameters among the different groups. Finally, we conducted a principal component analysis to detect the most valuable parameters to predict classes II and III and applied machine learning models.Methods This quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 583 Arab patients who were diagnosed as Class II or III according to the Calculated_ANB.Results The group comparison analysis showed that the most significant differences are available between different classes. Nevertheless, unlike many previous studies, we found differences between males and females within the same class. This was demonstrated in the parameters including NL-NSL angle, PFH/AFH ratio, SNB angle, SN-Pg angle, and ML-NSL angle of class III patients, but not in class II patients. Interestingly, this ethnic group of patients also revealed many differences in the different age groups within the same class; these differences were significant in the parameters NL-ML angle, ML-NSL angle, PFH/AFH ratio, facial axis, gonial angle, + 1/NA angle, + 1/NA (mm) in class II age groups, and + 1/NL angle, + 1/SNL angle, + 1/NA (mm), Wits appraisal, and interincisal angle the results showed that the Calculated_ANB correlated with many other cephalometric parameters when comparing two groups that belong to different classes. The Principal Component Analysis (PCA) results showed that we explained about 67% of the variation within the first two PCs. Finally, we used all parameters for the general Machine Learning (ML) model to calculate the importance of each parameter to the model. The stepwise forward Machine Learning models demonstrated the ability of the parameters Wits appraisal and SNB angle to predict the classification with 0.93 accuracy, compared to 0.95 accuracy when the general model predicted class II and III classifications.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.