East Asian and Southern European craniofacial class III phenotype: two sides of the same coin?
Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Cited by 3 publications
References 39 publications
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.
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.
Lateral Cephalometric Parameters Variations and Machine Learning Models Among Skeletal Class II & III Malocclusion of Arab Orthodontic Patients
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.
Evaluation of automated photograph-cephalogram image integration using artificial intelligence models
Objectives To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram. Materials and 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
Contact Info
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Product
Resources
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.
