This study aims to evaluate the ability of an artificial intelligence (AI) model developed for use in the field of orthodontics to accurately and reliably classify skeletal maturation stages of individuals using hand–wrist radiographs. A total of 809 grayscale hand–wrist radiographs (250 × 250 px; pre-peak n = 400, peak n = 100, post-peak n = 309) were analyzed using four complementary image-based feature extraction methods: Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Zernike Moments (ZM), and Intensity Histogram (IH). These methods generated 2355 features per image, of which 2099 were retained after variance thresholding. The most informative 1250 features were selected using the ANOVA F-test and classified with a stacking-based machine learning (ML) architecture composed of Light Gradient Boosting Machine (LightGBM) and Logistic Regression (LR) as base learners, and Random Forest (RF) as the meta-learner. Across all evaluation folds, the average performance of the model was Accuracy = 83.42%, Precision = 84.48%, Recall = 83.42%, and F1 = 83.50%. The proposed model achieved 87.5% accuracy, 87.8% precision, 87.5% recall, and an F1-score of 87.6% in 10-fold cross-validation, with a macro-average area under the ROC curve (AUC) of 0.96. The pre-peak stage, corresponding to the period of maximum growth velocity, was identified with 92.5% accuracy. These findings indicate that integrating handcrafted radiographic features with ensemble learning can enhance diagnostic precision, reduce observer variability, and accelerate evaluation. The model provides an interpretable and clinically applicable AI-based decision-support tool for skeletal maturity assessment in orthodontic practice.

In this study, an automatic classification of cervical vertebra maturation (CVM) stages was performed using raw lateral cephalometric radiographs to assess growth and development. A total of 4285 radiographs from the Department of Orthodontics at Van Yüzüncü Yıl University Faculty of Dentistry were utilized. Following detailed evaluations by specialist physicians, 3750 images meeting diagnostic accuracy and clinical suitability criteria were included. The selected images were categorized into six classes (CVMS 1–6), forming a balanced dataset for classification with the NFNet, ConvNeXt V2, EfficientNet V2, and DeiT3 models. The NFNet model achieved the highest overall performance, with 96% training accuracy and 85.7% test accuracy. ConvNeXt V2, attaining 95% training accuracy and 86.9% test accuracy, emerged as the most balanced in terms of generalization. Although EfficientNet V2 reached 94% training accuracy, its 80.7% test accuracy indicated limited generalization. With 93% training accuracy and 77.6% test accuracy, DeiT3 demonstrated the lowest capacity. Both NFNet and ConvNeXt V2 stood out as strong classification candidates based on their high accuracy and balanced performance. While NFNet showed a 10.3% gap between training and test accuracy, indicating somewhat reduced generalization, ConvNeXt V2’s narrower 8.1% gap suggested greater stability. In conclusion, NFNet and ConvNeXt V2 are promising models for CVM classification. Future studies should employ larger datasets and conduct hyperparameter optimization to enhance these models’ performance and strengthen their clinical applicability.

Background/Objectives: This study aims to evaluate mandibular condylar asymmetry in individuals with different types of skeletal malocclusions using a three-dimensional imaging technique, and to determine the relationship between these anomalies and condylar asymmetry. Methods: The study included 100 individuals who visited the Department of Orthodontics Faculty of Dentistry between 2015 and 2020 and underwent Cone-Beam Computed Tomography (CBCT) imaging for various reasons. The evaluation of condylar asymmetry was performed using the Habets method, and measurements were carried out with the NemoCeph V.2017 software. Participants were categorized into skeletal Class I (2–4°), Class II (>4°), and Class III based on their ANB angles. For statistical analysis, frequency distribution, the Kruskal–Wallis H test, and Spearman’s correlation coefficient were used. Results: No statistically significant relationship was found between gender and skeletal classifications (p > 0.05). In terms of age, the mean age of individuals in the Class III group was significantly lower than that of those in the Class II group (p < 0.05). A weak positive correlation was observed between condylar and ramal indices in the overall sample (p = 0.029); however, this correlation was found to be moderate and statistically significant only within the Class III group (p = 0.002). Conclusions: The presence of a significant relationship between condylar and ramal asymmetries in individuals with Class III malocclusion indicates an increased risk of developing facial asymmetry if left untreated. These findings underscore the importance of skeletal malocclusions in influencing condylar morphology.