12 research outputs found
Impacted Maxillary Canines - Etiology, Diagnosis, Radiologic and Orthodontic Clinical
No full textCanine maxillary teeth are aesthetically most dominant teeth which can present a smile as either holy or evi. They define the mouth’s corner, are involved in the aesthetic smile, maintain occlusal stability, and design the shape of the dental arch. As the position of the impacted canines is amidst vital anatomical structures like the nasal cavity and sinuses in the maxilla and mental nerve in the mandible, a thorough radiographic evaluation to determine the position of the impacted canines is of utmost importance prior to any treatment planning. By using a radiographic image, a clinician can clearly identify its location. The present paper aimed to determine the prevalence, etiology, and clinical diagnosis of impacted maxillary canines in orthodontic patients. Keywords: Canine impaction, Etiology, Inspection, Palpation, Radiographs, CT, CBCT, Transtomograph
Impacted Maxillary Canines - Etiology, Diagnosis, Radiologic and Orthodontic Clinical
No full textCanine maxillary teeth are aesthetically most dominant teeth which can present a smile as either holy or evi. They define the mouth’s corner, are involved in the aesthetic smile, maintain occlusal stability, and design the shape of the dental arch. As the position of the impacted canines is amidst vital anatomical structures like the nasal cavity and sinuses in the maxilla and mental nerve in the mandible, a thorough radiographic evaluation to determine the position of the impacted canines is of utmost importance prior to any treatment planning. By using a radiographic image, a clinician can clearly identify its location. The present paper aimed to determine the prevalence, etiology, and clinical diagnosis of impacted maxillary canines in orthodontic patients. Keywords: Canine impaction, Etiology, Inspection, Palpation, Radiographs, CT, CBCT, Transtomograph
Narrating the Genetic Landscape of Human Class I Occlusion: A Perspective-Infused Review
No full textThis 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
Narrating the Genetic Landscape of Human Class I Occlusion: A Perspective-Infused Review
No full textThis 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
Hierarchical clustering analysis & machine learning models for diagnosing skeletal classes I and II in German patients
No full textAbstract Background Classification is one of the most common tasks in artificial intelligence (AI) driven fields in dentistry and orthodontics. The AI abilities can significantly improve the orthodontist’s critical mission to diagnose and treat patients precisely, promptly, and efficiently. Therefore, this study aims to develop a machine-learning model to classify German orthodontic patients as skeletal class I or II based on minimal cephalometric parameters. Eventually, clustering analysis was done to understand the differences between clusters within the same or different skeletal classes. Methods A total of 556 German orthodontic patients were classified into skeletal class I (n = 210) and II (n = 346) using the individualized ANB. Hierarchical clustering analysis used the Euclidean distances between data points and Ward’s minimum variance method. Six machine learning models (random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA), classification and regression trees (CART), and General Linear Model (GLM)) were evaluated considering their accuracy, reliability, sensitivity, and specificity in diagnosing skeletal class I and II. Results The clustering analysis results showed the power of this tool to cluster the results into two–three clusters that interestingly varied significantly in many cephalometric parameters, including NL-ML angle, NL-NSL angle, PFH/AFH ratio, gonial angle, SNB, Go-Me (mm), Wits appraisal, ML-NSL, and part of the dental parameters. The CART model achieved 100% accuracy by considering all cephalometric and demographic variables, while the KNN model performed well with three input parameters (ANB, Wits, SNB) only. Conclusions The KNN model with three key variables demonstrated sufficient accuracy for classifying skeletal classes I and II, supporting efficient and still personalized orthodontic diagnostics and treatment planning. Further studies with balanced sample sizes are needed for validation
Cross-Sectional Observational Study of the Differences in Cephalometric Parameters in German Class I/II Orthodontic Patients
No full textLateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models
No full textBackground
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
No full text<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Objectives</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>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.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>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”).</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>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.</jats:p>
</jats:sec>
Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic Patients
No full textBackground: Orthodontic problems can affect vital quality of life functions, such as swallowing, speech sound production, and the aesthetic effect. Therefore, it is important to diagnose and treat these patients precisely. The main aim of this study is to introduce new classification methods for skeletal class I occlusion (SCIO) and skeletal class II malocclusion (SCIIMO) among Arab patients in Israel. We conducted hierarchical clustering to detect critical trends within malocclusion classes and applied machine learning (ML) models to predict classification outcomes. Methods: This study is based on assessing the lateral cephalometric parameters from the Center for Dentistry Research and Aesthetics based in Jatt, Israel. The study involved the encoded records of 394 Arab patients with diagnoses of SCIO/SCIIMO, according to the individualized ANB of Panagiotidis and Witt. After clustering analysis, an ML model was established by evaluating the performance of different models. Results: The clustering analysis identified three distinct clusters for each skeletal class (SCIO and SCIIMO). Among SCIO clusters, the results showed that in the second cluster, retrognathism of the mandible was less severe, as represented by a lower ANB angle. In addition, the third cluster had a lower NL-ML angle, gonial angle, SN-Ba angle, and lower ML-NSL angle compared to clusters 1 and 2. Among SCIIMO clusters, the results also showed that the second cluster has less severe retrognathism of the mandible, which is represented by a lower ANB angle and Calculated_ANB and a higher SNB angle (p < 0.05). The general ML model that included all measurements for patient classification showed a classification accuracy of 0.87 in the Random Forest and the Classification and Regression Tree models. Using ANB angle and Wits appraisal only in the ML, an accuracy of 0.78 (sensitivity = 0.80, specificity = 0.76) was achieved to classify patients as SCIO or SCIIMO. Conclusions: The clustering analysis revealed distinguished patterns that can be present within SCIO and SCIIMO patients, which can affect the diagnosis and treatment plan. In addition, the ML model can accurately diagnose SCIO/SCIIMO patients, which should improve precise diagnostics
Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients
No full textThe precise and efficient diagnosis of an individual’s skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations between various anatomic structures. The primary outcome of this prospective cross-sectional study was developing a machine learning model for classifying patients as skeletal class I and III. Furthermore, the investigation intended to compare cephalometric variables between skeletal class I and III as well as between age and sex-specific subgroups to analyse correlations between cephalometric parameters and to perform Principal Component Analysis (PCA) to identify the most important variables contributing to skeletal class I and III variances. This study was based on the pre-treatment lateral cephalograms of 509 German orthodontic patients diagnosed as skeletal class I (n = 341) or III (n = 168) according to the individualised ANB of Panagiotidis and Witt, following descriptive analyses of cephalometric parameters, correlation analyses followed by Principal Component Analysis (PCA) to identify key cephalometric variables. Machine learning models, including Random Forest (RF), Classification and Regression Trees (CART), k-nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Generalized Linear Model (GLM), were evaluated for accuracy. Within the same skeletal class, age influenced cephalometric parameters: in skeletal class I, adolescents presented a more horizontal pattern (PFH/AFH, Gonial angle, NL-ML) and prominent mandible (SNB, SN-Pg) than children. In skeletal class III, the degree of sagittal discrepancy between jaw bases was most notable in adults (ANB: III_Age > 21-III _14 < Age < 20 − 1.78°). Comparing skeletal class I and III, the latter had more prognathic mandibles (SNB) and compensated incisors’ inclination (proclination of the upper (+ 1/NA: 9.01°), retroinclination of the lower incisors (− 1/ML: 8.99°). Among others, a correlation was found between the sagittal (degree of prognathism, SNB) and vertical (inclination, ML-NSL) orientation of the mandible (skeletal class I: p < 0.001, ρ = − 0.742; skeletal class III: p < 0.001, ρ = − 0.665). PCA revealed that the first four principal components explain 93% of the variance in skeletal class I/III diagnosis and that these parameters had the most influence loading score on the first component-PFH/AFH ratio (0.35), SNB angle (0.35), SN-Pg (0.37), and ML-NSL (− 0.35). Evaluating machine learning models, the general model, including all cephalometric parameters, age, and sex, resulted in perfect (1.00) accuracy and kappa scores compared to the gold standard Calculated_ANB with the model’s RF and CART. In model 2 the amount of input variables was reduced (Wits, SNB only), but the accuracy (0.88), and kappa (0.73) were still good in the KNN model. In the last section of this study, we applied different machine learning classification models. We examined the ability of the parameters—SNA, SNB, and ML-NSL angles to predict the classification as skeletal class I or III. The results demonstrated that the GLM model gained an accuracy of 0.99 (Accuracy = 0.99, Kappa = 0.97). The precise diagnosis of skeletal class I/III can be simplified by applying the machine learning model GLM with the input variables SNA, SNB, and ML-NSL only. This stresses the importance of their correct identification. However, considering all skeletal classes, a larger population is needed to validate and generalize this approach
