Search Results (17)

Background/Objectives: Growth prediction may be used by the clinical orthodontist in growing individuals for diagnostic purposes and for treatment planning. This process appraises chronological age and determines the degree of skeletal maturity to calculate residual growth. In developmental deviations, overlooking such diagnostic details might culminate in erroneous conclusions, unstable outcomes, recurrence, and treatment failure. The present review aims to systematically present and explain the available means for predicting growth in humans. Traditional, long-known, popular methods are discussed, and modern digital applications are described. Materials and methods: A search on PubMed and the gray literature up to May 2025 produced 69 eligible studies on future maxillofacial growth prediction without any orthodontic intervention. Results: Substantial variability exists in the studies on growth prediction. In young orthodontic patients, the study of the lateral cephalometric radiography and the subsequent calculation of planes and angles remain questionable for diagnosis and treatment planning. Skeletal age assessment is readily accomplished with X-rays of the cervical vertebrae and the hand–wrist region. Computer software is being implemented to improve the reliability of classic methodologies. Metal implants have been used in seminal growth studies. Biochemical methods and electromyography have been suggested for clinical prediction and for research purposes. Conclusions: In young patients, it would be of importance to reach conclusions on future growth with minimal distress to the individual and, also, reduced exposure to ionizing radiation. Nevertheless, the potential for comprehensive prediction is still largely lacking. It could be accomplished in the future by combining established methods with digital technology. Full article

Accurate identification of growth and development stages is critical for orthodontic diagnosis, treatment planning, and post-treatment retention. While hand–wrist radiographs are the traditional gold standard, the associated radiation exposure necessitates alternative imaging methods. Lateral cephalometric radiographs, particularly the maturation stages of the second, third, and fourth cervical vertebrae (C2, C3, and C4), have emerged as a promising alternative. However, the nonlinear dynamics of these images pose significant challenges for reliable detection. This study presents a novel approach that integrates chaotic functional connectivity (FC) matrices and fractal dimension analysis to address these challenges. The fractal dimensions of C2, C3, and C4 vertebrae were calculated from 945 lateral cephalometric radiographs using three methods: fast Fourier transform (FFT), box counting, and a pre-processed FFT variant. These results were used to construct chaotic FC matrices based on correlations between the calculated fractal dimensions. To effectively model the nonlinear dynamics, chaotic maps were generated, representing a significant advance over traditional methods. Feature selection was performed using a wrapper-based approach combining k-nearest neighbors (kNN) and the Puma optimization algorithm, which efficiently handles the chaotic and computationally complex nature of cervical vertebrae images. This selection minimized the number of features while maintaining high classification performance. The resulting AI-driven model was validated with 10-fold cross-validation and demonstrated high accuracy in identifying growth stages. Our results highlight the effectiveness of integrating chaotic FC matrices and AI in orthodontic practice. The proposed model, with its low computational complexity, successfully handles the nonlinear dynamics in C2, C3, and C4 vertebral images, enabling accurate detection of growth and developmental stages. This work represents a significant step in the detection of growth and development stages and provides a practical and effective solution for future orthodontic diagnosis. Full article

Objectives: The present scoping review aims to provide a panoramic view of the current state of knowledge, highlighting the strengths, limitations, and future directions, on the use of CBCT in orthodontic practice. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines to identify eligible studies from the following databases: PubMed, Scopus, and Web of Science. The research question was formulated as follows: “What is the scientific evidence concerning the preferential use of 3D CBCT over 2D radiography in orthodontics”? Results: Through database searching, 521 records were identified, and ultimately, 37 studies that compared 3D CBCT with 2D conventional radiography were included. Of these, 16 articles regarded the use of CBCT for cephalometric analysis, 5 papers analyzed the evaluation of root resorption, 10 studies evaluated the diagnostic accuracy of root angulation and determining tooth position, and the remaining 6 articles were conducted for miscellaneous applications: determining the size of the nasopharyngeal airway (n = 2), miniscrew positioning (n = 1), estimating cervical vertebrae maturity (n = 1), and evaluating the correctness of the root location when placing digital indirect brackets (n = 1). Conclusions: The choice between 3D CBCT or CBCT-generated radiography and conventional 2D radiography in orthodontics involves careful consideration of the specific clinical context, the complexity of the case, and the balance between the diagnostic advantages and the associated limitations. Future Directions: Future studies with a prospective design and standardized imaging protocols are encouraged to facilitate the development of a consensus on the best practices. Full article

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research. Full article

With the changes in human work and lifestyle, the incidence of cervical spondylosis is increasing substantially, especially for adolescents. Cervical spine exercises are an important means to prevent and rehabilitate cervical spine diseases, but no mature unmanned evaluating and monitoring system for cervical spine rehabilitation training has been proposed. Patients often lack the guidance of a physician and are at risk of injury during the exercise process. In this paper, we first propose a cervical spine exercise assessment method based on a multi-task computer vision algorithm, which can replace physicians to guide patients to perform rehabilitation exercises and evaluations. The model based on the Mediapipe framework is set up to construct a face mesh and extract features to calculate the head pose angles in 3-DOF (three degrees of freedom). Then, the sequential angular velocity in 3-DOF is calculated based on the angle data acquired by the computer vision algorithm mentioned above. After that, the cervical vertebra rehabilitation evaluation system and index parameters are analyzed by data acquisition and experimental analysis of cervical vertebra exercises. A privacy encryption algorithm combining YOLOv5 and mosaic noise mixing with head posture information is proposed to protect the privacy of the patient’s face. The results show that our algorithm has good repeatability and can effectively reflect the health status of the patient’s cervical spine. Full article

This paper aims to propose a statistical model to assess pubertal growth spurt using the ratio of the anterior height projection to the posterior (Vba) of the fourth cervical vertebra body (C4) on cephalograms and to calculate the residual proportion of skeletal maturation and the time for the pubertal growth spurt to end for a given Vba. A sample of 538 cephalograms from healthy-living children aged between 5 and 15 years was analyzed. A segmented regression model was used to explain the different Vba stages relative to the pubertal growth spurt. In addition, the time to achieve skeletal maturation was evaluated for a given Vba between the beginning (Vba₁) and the end (Vba₂) of the pubertal growth spurt. A longitudinal sample of 25 males and 25 females was analyzed to validate the proposed method. The values of Vba corresponding to higher pubertal development rate ranged from Vba₁ = 0.677 (95%CI, 0.644–0.711) to Vba₂ = 0.966 (95%CI, 0.905–1.028) and from Vba₁ = 0.669 (95%CI, 0.645–0.693) to Vba₂ = 1.073 (95%CI, 1.044–1.101) in males and females, respectively. The validation process results showed that our model did not produce any incorrect forecasts. The proposed method estimates the beginning and the end of the pubertal growth spurt together with the residual proportion of skeletal maturation for a given Vba. Full article

Accurate determination of skeletal maturation indicators is crucial in the orthodontic process. Chronologic age is not a reliable skeletal maturation indicator, thus physicians use bone age. In orthodontics, the treatment timing depends on Cervical Vertebral Maturation (CVM) assessment. Determination of CVM degree remains challenging due to the limited annotated dataset, the existence of significant irrelevant areas in the image, the huge intra-class variances, and the high degree of inter-class similarities. To address this problem, researchers have started looking for external information beyond current available medical datasets. This work utilizes the domain knowledge from radiologists to train neural network models that can be utilized as a decision support system. We proposed a novel supervised learning method with a multi-scale attention mechanism, and we incorporated the general diagnostic patterns of medical doctors to classify lateral X-ray images as six CVM classes. The proposed network highlights the important regions, surpasses the irrelevant part of the image, and efficiently models long-range dependencies. Employing the attention mechanism improves both the performance and interpretability. In this work, we used additive spatial and channel attention modules. Our proposed network consists of three branches. The first branch extracts local features, and creates attention maps and related masks, the second branch uses the masks to extract discriminative features for classification, and the third branch fuses local and global features. The result shows that the proposed method can represent more discriminative features, therefore, the accuracy of image classification is greater in comparison to in backbone and some attention-based state-of-the-art networks. Full article

Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists. Full article

The research aims to verify the concordance between the skeletal maturity index (SMI) measured on the hand and wrist X-rays using Fishman method and the cervical vertebral maturation stage (CVMS), measured on the lateral cephalometric X-rays using Baccetti method. The concordance of the two indexes (SMI and CVMS) has been statistically verified with the help of the Cohen’s kappa coefficient, by relating them to the growth stages, within a longitudinal study done upon a group of 38 patients, 22 female and 16 male, aged between 8–18 y, the analyzed investigations being done in series, along the orthodontic treatment. The research showed a strong correlation between the SMI and CVMS indexes within the analyzed group, confirmed by the obtained values (k = 0.84 for female and k = 0.85 for male). Full article

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based. Full article

This retrospective observational study aimed to examine the correlation and correspondence between skeletal maturation indicators (SMI), cervical vertebral maturation indicators (CVMI), and radius-ulna-short bones (RUS) skeletal maturity scores in Korean adolescents, and to determine whether easily obtainable SMI or CVMI can replace the RUS skeletal maturity score. A total of 1017 participants were included with both hand-wrist radiograph and lateral cephalogram acquired concurrently. From the lateral cephalogram, CVMI was determined; through the hand-wrist radiograph, SMI was categorized, and the RUS skeletal maturity score was evaluated as well. Associations were examined using the Mann–Whitney U test, Spearman’s rank-order correlation analysis, and multiple correspondence analysis. There was no statistically significant difference in chronological age between males and females; however, the SMI, CVMI, and RUS skeletal maturity scores were significantly higher in females. The SMI, CVMI, and RUS skeletal maturity scores showed a statistically significant strong degree of both positive correlation and correspondence. However, a precisely corresponding RUS skeletal maturity score was difficult to obtain for a specific CVMI and SMI stage, implying the absence of a quantitative correlation. In conclusion, detailed evaluation should be conducted using the RUS skeletal maturity score, preferably in cases that require bone age determination or residual growth estimation. Full article

The purpose of this study is to evaluate and compare the performance of six state-of-the-art convolutional neural network (CNN)-based deep learning models for cervical vertebral maturation (CVM) on lateral cephalometric radiographs, and implement visualization of CVM classification for each model using gradient-weighted class activation map (Grad-CAM) technology. A total of 600 lateral cephalometric radiographs obtained from patients aged 6–19 years between 2013 and 2020 in Pusan National University Dental Hospital were used in this study. ResNet-18, MobileNet-v2, ResNet-50, ResNet-101, Inception-v3, and Inception-ResNet-v2 were tested to determine the optimal pre-trained network architecture. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve (AUC) values from the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models. All deep learning models demonstrated more than 90% accuracy, with Inception-ResNet-v2 performing the best, relatively. In addition, visualizing each deep learning model using Grad-CAM led to a primary focus on the cervical vertebrae and surrounding structures. The use of these deep learning models in clinical practice will facilitate dental practitioners in making accurate diagnoses and treatment plans. Full article

The aim of this study was to investigate the reproducibility of the Cervical Vertebral Maturation (CVM) method and the potential for chronological age estimation using this method. The sample consisted of 474 lateral cephalometric radiographs, from orthodontic patients aged 6.4–22.4 years. Six raters were trained to the CVM method (Baccetti). All images were assessed twice. Intra- and inter-rater agreements were assessed by Cohen’s weighted kappa and intraclass correlation coefficient, respectively. Analysis of variance was performed to investigate the correlation between cervical maturation stages and chronological age. The age prediction potential of the method was tested by general linear model regression analysis. Intra-rater reliability ranged from 0.857 to 0.931. Intra-rater absolute agreement ranged from 77% to 87% however inter-rater absolute agreement was lower than 50%. Inter-rater reliability was higher than 0.9. The 3rd Cervical Maturation Stage (CS3) showed the lowest reproducibility. The mean age differences among the 6 CS stages were statistically significant and increased as the CS increased. CS and gender could roughly explain the 60% (adjusted R² = 0.61) of the age variance of the sample. This CVM method proved able to show high reliability; however, it cannot predict accurately the pubertal growth spurt. A direct correlation was found between cervical stages and chronological age. This method provides a broad estimation of chronological age. Full article

The assessment of an individual’s development by investigating the skeletal maturity is of much use in various medical fields. Skeletal maturity can be estimated by evaluating the morphology of the cervical vertebrae. The aim of this study was to conduct comparisons of the chronological age in different bone development stages. The retrospective study was conducted based on lateral cephalometric radiographs belonging to patients with ages between 6 and 15.9 years, from Romania. For the assessment of skeletal maturity, the Cervical Vertebral Maturation (CVM) method was used. In total, 356 radiographs were selected, but after applying the exclusion criteria, 252 radiographs remained in the study (178 girls and 74 boys). Different mean chronological age values were obtained for the general sample, as well as for the two genders. The chronological age started to be significantly different at the CS4 stage. Patients with CS4, CS5, and CS6 stages had a significantly higher chronological age compared to patients with CS1, CS2, and CS3 stages. It was noted that patients with CS1 and CS2 stages were more frequently boys, while patients with the CS5 stage were more frequently girls. Full article

Aim: The treatment of patients with mixed dentition, with inferior moderate dental crowding (the so-called borderline cases, between extraction and expansion) is not yet clear. Two examples of widely used appliances for increasing lower dental arch dimensions are the Schwarz’s appliance and lip bumper. The aim of this prospective study was to compare dental crowding and arch dimensions from pre- to post-treatment with lip bumper versus Schwarz’s appliance. Subjects and Methods: Pre- and post-treatment orthodontic records of twenty subjects (10 males and 10 females) were analyzed in the present study. Inclusion criteria were: first/second molar class malocclusion; crowding of the mandibular arch, from mild to moderate (4–6 mm); mixed dentition; age ≤ 9 years at the beginning of the treatment; stage CS1 or CS2 of maturation of the cervical vertebrae analysis (CVM) at the beginning of the treatment. Ten subjects were treated with a lip bumper, and ten with the removable Schwarz appliance. The primary outcomes were the variations in dental crowding and arch dimensions from pre- to post-treatment. Results: Both the two appliances caused a statistically significant mean improvement/reduction in crowding, of 3.5 mm and 2.9 mm, for the Schwarz appliance and lip bumper, respectively. The Schwarz appliance resulted more effective in increasing arch dimension at the intercanine level, and arch perimeter, while the lip bumper achieves a higher increase in arch length. Conclusions: A lip bumper and Schwarz appliance are both useful in reducing crowding in mixed dentition. This improvement is due to the increase in dental arch dimensions, although the distribution of space resulted slightly differently between the two appliances. Full article