Search Results (31)

Background: Digital intraoral scanners (IOSs) represent one of the key tools in contemporary digital dentistry and are widely applied in prosthodontics, orthodontics, implantology, and treatment monitoring. The aim of this study was to critically analyze the factors affecting the accuracy of intraoral scanning and to evaluate their clinical significance. Methods: This study was designed as a narrative literature review conducted using a structured literature search. The analysis was based on publications retrieved from the PubMed and ScienceDirect databases and published between 2016 and 2026 using keywords related to intraoral scanners, digital impressions, trueness, precision, scanning strategy, operator experience, scanner type, restorative materials, and clinical conditions. Results: Twenty publications selected according to the review scope and eligibility criteria were included. Based on the analysis, four major groups of factors influencing IOS accuracy were identified: scanning strategy, operator experience, scanner type and restorative material, and clinical/environmental conditions. The available evidence most consistently associated scan quality with the scanner type and the applied scanning strategy, although the effectiveness of individual protocols depended on the specific IOS system used. The influence of operator experience remained inconclusive and appeared to be less significant in modern devices equipped with advanced image reconstruction algorithms. Clinical factors such as lighting conditions, arch morphology, subgingival preparation margins, maneuverability restrictions, and material-related optical properties also played an important role, whereas humidity and moisture control should be interpreted as possible clinical confounders rather than experimentally verified independent factors in the included primary studies. Conclusions: The findings indicate that intraoral scanning accuracy is a multifactorial phenomenon requiring individualized adaptation of the scanning procedure to clinical conditions and the type of IOS system used. Because the included evidence was heterogeneous and did not allow for formal ranking of effect magnitude, conclusions regarding the relative importance of individual factors should be interpreted with caution. Full article

Background/Objectives: Facial deformities compromise craniofacial aesthetics and functionality. Maxillofacial surgery and orthodontics require meticulous planning utilizing tomography and clinical photography. Although tomography provides precise information about bony structures, it has limitations in evaluating soft tissues. Three-dimensional photography emerges as a complementary alternative; however, there is limited information regarding morphological facial analysis in three dimensional (3D) images. Methods: A scoping review was conducted according to PRISMA-ScR guidelines and the Joanna Briggs Institute methodological framework. The MEDLINE (PubMed), Scopus, SciELO, and Web of Science databases were searched for studies published between 2017 and 2025 evaluating the use of 3D facial photography in orthodontics, orthognathic surgery, maxillofacial surgery, and related craniofacial applications. Two independent reviewers performed study selection and data extraction, with disagreements resolved by a third reviewer. Anatomical landmarks and facial measurements were descriptively synthesized according to facial regions and measurement type. Results: Out of 79 initially identified articles, 18 observational studies examining 3D facial photographs were included. Consequently, the “3D FullFace A” tool was developed, incorporating 13 midline points and 12 bilateral points, enabling 18 linear and 28 angular measurements for comprehensive facial analysis. Conclusions: Available evidence suggests that 3D facial photography is a promising tool for orthognathic surgical planning. Key advances identified include reliable stereophotogrammetric landmark identification, quantification of soft-tissue changes following orthognathic and orthodontic treatment, and characterization of facial asymmetry and proportionality. Full article

Background/Objectives: Tooth impaction and transposition are developmental dental anomalies that pose significant challenges in orthodontics, oral surgery, and restorative dentistry. These anomalies disrupt normal occlusal development and complicate diagnosis and treatment planning because of altered anatomy, a higher risk of adjacent tooth resorption, associated pathology such as cyst formation, and biomechanical challenges during orthodontic intervention. Methods: Three paediatric patients with complex eruption disturbances, including impacted incisors, impacted primary molars, and transposed maxillary canines, were managed using individualised conservative orthodontic approaches. The cases involved obstruction by supernumerary teeth, ectopic eruption with space loss, and tooth transposition complicated by root dilaceration and malocclusion. Comprehensive clinical and radiographic assessment, including CBCT when indicated, guided treatment planning. Management strategies included staged surgical interventions, space maintenance using 2 × 4 appliances, bite opening, and controlled orthodontic traction with modified biomechanics to minimise the risk of root resorption and interference with adjacent teeth. Results: Favourable functional and aaesthetic outcomes were achieved in all cases, although certain limitations such as incomplete root development, residual spacing, localised restorative complications, and difficulty in correcting root angulation of dilacerated adjacent teeth were observed. Conclusions: Careful diagnosis using advanced imaging, combined with conservative orthodontic and surgical approaches, can achieve satisfactory functional and esthetic outcomes while minimising complications. Further longitudinal studies are recommended to evaluate long-term stability and optimise treatment protocols. Full article

Objectives: To explore volumetric changes in the nasopharynx, oropharynx, hypopharynx, and total pharyngeal airway after Class II functional orthodontic treatment and to relate these anatomical changes to patient-reported breathing and sleep comfort. Methods: A retrospective observational study of 63 growing patients (11–14 years) with Class II mandibular retrusion was conducted. Pre- and post-treatment cone beam computed tomography (CBCT) scans (NewTom 5G) were exported as uncompressed digital imaging and communications in medicine (DICOM) files and analyzed with Dolphin Imaging v11.9. The pharyngeal airway was manually segmented into naso-, oro-, and hypopharyngeal regions; volumes were measured twice by a single examiner to assess reliability. Inter-examiner reliability was assessed in a 15-patient subgroup. Patients completed a 5-point Likert rating of perceived breathing and sleep changes. Statistical tests included Wilcoxon signed-rank, Friedman with Holm post hoc, Mann–Whitney U, and Spearman correlation; p < 0.05 was considered significant. Results: Intra-examiner reliability was excellent (ICC1 mean 0.988), and inter-examiner reliability was good to excellent (ICC2 0.87–0.95). Mean total pharyngeal airway volume during the treatment interval in growing Class II patients treated with functional appliances increased from 18,183.62 mm³ at T0 to 23,524.42 mm³ at T1—an average augmentation of approximately 5340.80 mm³ (~29.4%). Regional gains followed the following pattern: Oropharynx > Nasopharynx > Hypopharynx. Nasopharyngeal and total airway changes were found to be associated with improvements in reported breathing (ρ = 0.79 and 0.69) and sleep comfort (total airway ρ = 0.74). Conclusions: Functional mandibular advancement in growing Class II patients was associated with enlargement of the pharyngeal airway—most notably in the oropharynx—and these volumetric gains were followed by improved patient-reported breathing and sleep comfort within the limitations of retrospective study design. Full article

Background: Diagnosis is the fundamental basis for understanding biomechanics in orthodontic treatment and for accurately designing the treatment plan. Traditionally, the sagittal plane has been the primary focus of assessment; however, it is essential to consider the patient in all three spatial planes. Therefore, it is necessary to explore the transverse plane, which is equally as crucial as the sagittal and vertical planes. With current technological advances, it is now possible to obtain three-dimensional images of the patient using cone-beam computed tomography (CBCT), allowing evaluation of all planes in a single diagnostic test. This study aimed to assess the diagnostic methods used for transverse analysis and the usefulness of CBCT for this purpose. Material and Methods: To select the studies for this review, we searched the PubMed, Scopus, and Cochrane databases for publications between 1965 and 2021. Our inclusion criteria targeted studies that evaluated the transverse plane using CBCT or CT. We assessed the level of evidence according to the OCEBM classification and evaluated the risk of bias using the QUADAS-2 scale. Results: After reviewing 535 articles, we selected 16 that met the established criteria. These studies compared various diagnostic methods for transverse analysis and their reproducibility indices. We identified the absence of a gold standard for measuring transverse discrepancies and high variability among diagnostic methods as the main limitations. Conclusions: Based on the available evidence, it can be concluded that dental and skeletal transverse discrepancies can be reliably differentiated using the diagnostic techniques evaluated in this study, particularly through CBCT-based assessment. Therefore, the diagnosis of transverse discrepancies should not be considered unclear, as it can be established using objective and measurable criteria. These findings reinforce the clinical value of current diagnostic tools and highlight the importance of accurate three-dimensional interpretation for informed and effective treatment decision-making. Full article

Background: Artificial intelligence (AI) integrated with cone-beam computed tomography (CBCT) has rapidly advanced the diagnostic capability of orthodontics, particularly for quantifying external root resorption (ERR). High-risk scenarios such as bilateral maxillary canine impaction require objective tools to guide treatment decisions and prevent irreversible damage. Objectives: To evaluate the diagnostic accuracy and clinical applicability of AI-assisted CBCT for orthodontically induced ERR, and to demonstrate its value in a complex clinical case where decision-making regarding canine traction versus extraction required precise risk quantification and definition of biological limits. Methods: A systematic review following PRISMA 2020 guidelines was conducted in PubMed, ScienceDirect, and Cochrane Library (2015–September 2025). Eligible studies applied AI-enhanced CBCT to assess ERR in orthodontic patients. Additionally, a clinical case with bilaterally impacted maxillary canines was evaluated using CBCT with automated AI segmentation and manual refinement to quantify root volume changes and determine prognostic thresholds for treatment modification. Results: Nine studies met the inclusion criteria. AI-based imaging, predominantly convolutional neural networks, showed high diagnostic accuracy (up to 94%), improving reproducibility and reducing operator dependency. In the clinical case, volumetric monitoring showed rapid progression of ERR in the lateral incisors (LI) associated with a persistent unfavorable 3D spatial relationship between the canines and incisor roots, despite controlled distal traction with skeletal anchorage, leading to a timely change in the treatment plan and extraction of the severely compromised incisors with substitution by the canines. AI-generated data provided objective evidence supporting safer decision-making and prevented further structural deterioration. Conclusions: AI-enhanced CBCT enables early, objective, and quantifiable ERR assessment, strengthening prognosis-based decisions in orthodontics. Findings of this review and the clinical case highlight the translational relevance of AI for managing high-risk cases, such as maxillary canine impaction with extensive LI resorption, supporting future predictive AI models for safer canine traction. Full article

Background/Objectives: The sella turcica (ST) is a central craniofacial and endocrinological landmark whose morphology reflects both local skeletal development and systemic influences. Alterations in its form have been observed in various genetic syndromes, including trisomy 21 (Down syndrome, DS). Considering the characteristic craniofacial morphology of DS, this review aimed to evaluate whether individuals with DS present distinctive morphometric features and shape variants of the ST compared with non-syndromic populations and to discuss their diagnostic and clinical relevance. Methods: A systematic literature search was carried out in PubMed, the Cochrane Library, Web of Science, Wiley, MDPI, and Google Scholar on 8 May 2024. Search terms included “sella turcica,” “Down syndrome,” and “morphology.” Studies employing lateral cephalograms, cone-beam computed tomography (CBCT), or computed tomography (CT) to assess ST morphology were included when quantitative or qualitative comparisons with control groups were available. The review followed the PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD42024580071). Results: Only six studies fulfilled the inclusion criteria. Increased ST dimensions and a predominance of U-shaped and J-shaped variants in individuals with DS compared with controls were most frequently reported. Although the studies differed in methodology, the findings consistently indicated characteristic enlargement and remodeling of the ST in trisomy 21. Conclusions: Individuals with Down syndrome exhibit distinctive sella turcica morphology characterized by increased size and specific shape variants. The evidence base remains small and heterogeneous, with few observational studies and mixed age groups and imaging modalities, which limits the strength and generalizability of the conclusions. The present study aims to provide a modern, updated systematic review of current evidence on sella turcica morphology in patients with Down syndrome, to identify reported patterns of variation, and to explore their clinical and diagnostic significance. Recognition of these features enhances diagnostic accuracy in craniofacial evaluation, facilitates comprehensive orthodontic, endocrine, and oncological assessment, and advances understanding of cranial base development within the context of genetic syndromes. Full article

Objectives: There is a growing interest among practitioners in employing artificial intelligence (AI) to enhance the precision and efficiency of diagnostic methods. The objective of this study is to assess the precision of an AI-based method for facial asymmetry assessment using 3D facial images. Methods: The study included 130 patients (84 female, 46 male), analyzing 3D facial images from the Vectra^® M3 imaging system using both manual and AI-based methods. Seven bilateral facial landmarks were identified for manual analysis, calculating the asymmetry index for facial symmetry assessment. An AI-based program was developed to automate the identification of the same landmarks and calculate the asymmetry index. The reliability of the manual measurements was assessed using intraclass correlation coefficients (ICC) with 95% confidence intervals (CI). Precision of automated landmark identification was compared to the manual method. Results: The ICCs for the manual measurements demonstrated moderate to excellent reliability, both within raters (ICC = 0.62–0.99) and between raters (ICC = 0.72–0.96) each calculated with 95% CI. Agreement was observed between the manual and automated methods in calculating the asymmetry index for five landmarks. There was a statistically significant difference between the two methods in determining the asymmetry index for alare (median: 2.05 mm manual vs. 1.54 mm automated, p = 0.0056) and cheilion (median: 2.77 mm manual vs. 2.30 mm automated, p = 0.0081). Conclusions: The AI-based method provides efficient and comparable precision of facial asymmetry analysis using 3D images. The disagreement observed between the two methods can be addressed through further improvement and training of the automated software. This innovative approach opens doors to significant advancements in both research and clinical orthodontics. Full article

Background: Artificial intelligence (AI) is influencing pediatric dentistry by supporting diagnostic accuracy, optimizing treatment planning, and improving patient care, especially for children with special needs. Previous studies explored various aspects of AI in pediatric dentistry and special care dentistry, predominantly focusing on clinical implementation or technical advancements. However, no prior review has specifically addressed its application at the intersection of pediatric dentistry and special care dentistry, particularly with respect to ethical and environmental perspectives. Objective: This scoping review provides a comprehensive synthesis of AI technologies in pediatric dentistry with a dedicated focus on children with special health care needs. It aims to critically evaluate current applications and examine the clinical, ethical, and environmental implementation challenges unique to these populations. Methods: A structured literature search was conducted in PubMed, Scopus, and Web of Science from inception to August 2025, using predefined inclusion and exclusion criteria. Eligible studies investigated AI applications in pediatric dental care or special needs contexts. Studies were synthesized narratively according to thematic domains. Results: Sixty-five studies met the inclusion criteria. Thematic synthesis identified nine domains of AI application: (1) diagnostic imaging and caries detection, (2) three-dimensional imaging, (3) interceptive and preventive orthodontics, (4) chatbots and teledentistry, (5) decision support, patient engagement and predictive analytics, (6) pain assessment and discomfort monitoring, (7) behavior management, (8) behavior modeling, and (9) ethical considerations and challenges. The majority of studies were conducted in general pediatric populations, with relatively few specifically addressing children with special health care needs. Conclusions: AI in pediatric dentistry is most developed in diagnostic imaging and caries detection, while applications in teledentistry and predictive analytics remain emerging, and areas such as pain assessment, behavior management, and behavior modelling are still exploratory. Evidence for children with special health care needs is limited and seldom validated, highlighting the need for focused research in this group. Ethical deployment of AI in pediatric dentistry requires safeguarding data privacy, minimizing algorithmic bias, preventing overtreatment, and reducing the carbon footprint of cloud-based technologies. Full article

Digital dentistry is undergoing rapid evolution, with three-dimensional imaging technologies increasingly integrated into routine clinical workflows. Originally developed for accurate dental arch reconstruction, modern intraoral scanners have demonstrated expanding versatility in capturing intraoral mucosal as well as perioral cutaneous structures. Concurrently, photogrammetry has emerged as a powerful method for full-face digital reconstruction, particularly valuable in orthodontic and prosthodontic treatment planning. These advances offer promising applications in forensic sciences, where high-resolution, three-dimensional documentation of anatomical details such as palatal rugae, lip prints, and bite marks can provide objective and enduring records for legal and investigative purposes. This study explores the forensic potential of two digital acquisition techniques by presenting two cadaveric cases of animal bite injuries. In the first case, an intraoral scanner (Dexis 3600) was used in an unconventional extraoral application to directly scan skin lesions. In the second case, photogrammetry was employed using a digital single-lens reflex (DSLR) camera and Agisoft Metashape, with standardized lighting and metric scale references to generate accurate 3D models. Both methods produced analyzable digital reconstructions suitable for forensic archiving. The intraoral scanner yielded dimensionally accurate models, with strong agreement with manual measurements, though limited by difficulties in capturing complex surface morphology. Photogrammetry, meanwhile, allowed for broader contextual reconstruction with high texture fidelity, albeit requiring more extensive processing and scale calibration. A notable advantage common to both techniques is the avoidance of physical contact and impression materials, which can compress and distort soft tissues, an especially relevant concern when documenting transient evidence like bite marks. These results suggest that both technologies, despite their different origins and operational workflows, can contribute meaningfully to forensic documentation of bite-related injuries. While constrained by the exploratory nature and small sample size of this study, the findings support the viability of digitized, non-destructive evidence preservation. Future perspectives may include the integration of artificial intelligence to assist with morphological matching and the establishment of digital forensic databases for pattern comparison and expert review. 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

Precision in diagnosis is essential for achieving optimal outcomes in prosthodontics, orthodontics, and orthognathic treatments. Virtual articulators provide a sophisticated digital alternative to conventional methods, integrating intraoral scans, facial scans, and cone beam computed tomography (CBCT) to enhance treatment predictability. This review examines advancements in virtual articulator technology, including digital workflows, virtual facebow transfer, and occlusal analysis, with a focus on Artificial Intelligence (AI)-driven methodologies such as machine learning and artificial neural networks. The clinical implications, particularly in condylar guidance and sagittal condylar inclination, are investigated. By streamlining the acquisition and articulation of digital dental models, virtual articulators minimize material handling errors and optimize workflow efficiency. Advanced imaging techniques enable precise alignment of digital maxillary models within computer-aided design and computer-aided manufacturing systems (CAD/CAM), facilitating accurate occlusal simulations. However, challenges include potential distortions during digital file integration and the necessity for robust algorithms to enhance data superimposition accuracy. The adoption of virtual articulators represents a transformative advancement in digital dentistry, with promising implications for diagnostic precision and treatment outcomes. Nevertheless, further clinical validation is essential to ensure the reliable transfer of maxillary casts and refine digital algorithms. Future developments should prioritize the integration of AI to enhance predictive modeling, positioning virtual articulators as a standard tool in routine dental practice, thereby revolutionizing treatment planning and interdisciplinary collaboration. This review explores advancements in virtual articulators, focusing on their role in enhancing diagnostic precision, occlusal analysis, and treatment predictability. It examines digital workflows, AI-driven methodologies, and clinical applications while addressing challenges in data integration and algorithm optimization. Full article

Measurements of tooth size for estimating inter-arch tooth size discrepancies and inter-tooth distances, essential for orthodontic diagnosis and treatment, are primarily done using traditional methods involving plaster models and calipers. These methods are time-consuming and labor-intensive, requiring multiple steps. With advances in cone-beam computerized tomography (CBCT) and intraoral scanning technology, these processes can now be automated through computer analyses. This study proposes a multi-step computational method for measuring mesiodistal tooth widths and inter-tooth distances, applicable to both CBCT and scan images of plaster models. The first step involves 3D segmentation of the upper and lower teeth using CBCT, combining results from sagittal and panoramic views. For intraoral scans, teeth are segmented from the gums. The second step identifies the teeth based on an adaptively estimated jaw midline using maximum intensity projection. The third step uses a decentralized convolutional neural network to calculate key points representing the parameters. The proposed method was validated against manual measurements by orthodontists using plaster models, achieving an intraclass correlation coefficient of 0.967 and a mean absolute error of less than 1 mm for all tooth types. An analysis of variance test confirmed the statistical consistency between the method’s measurements and those of human experts. Full article

Background: The orthodontic management of pediatric patients with rare diseases, such as Ectodermal Dysplasia (ED) and Osteogenesis Imperfecta (OI), requires complex protocols due to dental anomalies in both the number and structure of teeth. These conditions necessitate a departure from traditional orthodontic approaches, as skeletal anchoring is often required because of these anomalies. Case Presentation: A patient with ED, characterized by hypodontia and malformed teeth, presented with insufficient natural teeth for anchorage. This challenge was addressed using a Maxillary Skeletal Expander (MSE) with miniscrews. Cone-beam computed tomography (CBCT) and cephalometric radiographs were used to assess bone density, which guided the creation of a customized hybrid device. A second patient with OI, a condition causing fragile bones, had malformed teeth and a high risk of fractures. Skeletal anchoring with MSE and miniscrews was chosen to avoid damaging brittle bones. The fragile nature of the patient’s bones required careful planning and close monitoring throughout the treatment process. Both patients were treated at the UOC of Pediatric Dentistry, Sapienza University of Rome, using MSE with miniscrews. Pre- and post-treatment imaging (CBCT and cephalometric radiographs) were used to evaluate bone quality and monitor progress. Skeletal anchoring successfully addressed the unique challenges in both cases, achieving outcomes comparable to those in unaffected patients. Discsussions: despite limited bone volume, MSE successfully achieved maxillary arch expansion and improved occlusion. Post-treatment radiographs showed successful maxillary expansion and alignment without complications. Conclusions: This case series highlighted the effectiveness of MSE with miniscrews in treating patients with rare diseases. It advances orthodontic management by offering reliable solutions for complex cases involving dental anomalies and compromised bone structures. Full article

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle–Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists. Full article