Background: Orthodontics and orthognathic surgery present challenges such as extended treatment durations, patient discomfort, and complications like root resorption. Recent advancements in tissue engineering and nanotechnology offer promising solutions by improving bone regeneration, periodontal repair, and biomaterial integration. Objectives: This review explores the integration of scaffold-based tissue engineering and nanotechnology in orthodontics, focusing on their roles in accelerating bone regeneration, reducing treatment times, and minimizing adverse effects to enhance the predictability and success of orthodontic interventions. Methods: Relevant literature was selected from PubMed, Scopus, and Web of Science, focusing on studies related to scaffold technology, biomaterials, and nanotechnology in orthodontics. Keywords included “tissue engineering”, “orthodontics”, “biomaterials”, “scaffolds”, “nanotechnology”, and “bone regeneration”. Priority was given to peer-reviewed original studies, systematic reviews, and meta-analyses addressing innovative treatment approaches and clinical outcomes. Results: Findings indicate that scaffolds enhance bone regeneration and periodontal repair, while nanoparticles improve biomaterial integration and drug delivery efficiency. These advancements contribute to faster, more predictable orthodontic treatments with reduced complications. However, challenges such as high costs, regulatory hurdles, and the need for long-term clinical validation remain barriers to widespread adoption. Conclusions: Tissue engineering and nanotechnology offer minimally invasive, biologically driven solutions for orthodontic treatment. While significant progress has been made, further clinical studies, cost-effective strategies, and regulatory approvals are needed to integrate these innovations into routine practice.

A bibliometric analysis uses statistical and mathematical methods to analyze the published literature, extracting meaningful information that helps to identify trends, assess research impact, and visualize key data patterns through graphs and trendlines. The aim of the study was to list the top 50 papers on clear aligners, evaluate them critically and apply a bibliometric analysis to investigate the achievements and prospects in this field of study. A computerized database search (Scopus) was conducted on September 24, 2023, to find articles on clear aligners published in the scientific literature between 2013 and 2023. The top 50 cited manuscripts were chosen. Author-based characteristics were obtained from the Scopus database. The dataset from Clarivate™ InCites Benchmarking & Analytics™ and Journal Citation Reports (JCR) was used to determine parameters based on journals and articles. To improve the visual analysis, the keywords were collected systematically from the chosen articles. The database search produced a directory of the 50 most often cited articles out of 1,405 papers. Of the 50 most frequently cited works on clear aligners, 35 (70.0%) were original research studies and 15 (30.0%) were reviews. The keyword-network analysis indicated 'removable orthodontic appliance' as the most important and repetitive keyword. The results of the present bibliometric study indicated that clear aligner therapy (CAT)-related papers received increasing citations. The study determined the most influential articles by highlighting their authors and the journals in which the papers were published.

Background/Objectives: The aim of this study was to quantify the threshold of a dental midline shift that would compromise facial attractiveness and indicate a need for treatment from the points of view of laypeople and dental professionals. Methods: Whole-face natural photographs of a male and a female model were digitally manipulated to create various degrees of upper and lower dental midline shifts through bodily movement of the upper or lower midlines as well as alteration of the axial inclination of the upper teeth. The samples were then assessed by two groups of observers (laypeople (LP) and dental professionals (DP)). Results: The lower midline shift did not negatively affect the DP and LP’s perceptions of smile attractiveness. The first significant loss of attractiveness was registered by the DP with an upper midline shift of 1 mm in the female model. However, the LP registered this at 2 mm. The DP registered the necessity of treatment at a threshold of 2 mm in the female model and 3 mm in the male model. LP identified the need for treatment at 3 mm for both males and females. The female model was judged more critically than her male counterpart by both female and male observers. Conclusions: DP assess the midline deviation more critically than LP. Both DP and LP were more sensitive to midline deviations in the female model regardless of their own gender. Both groups registered the need for treatment at a higher threshold than the reduction in smile attractiveness.

Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP₅₀ was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness.

Purpose: This study aimed to evaluate the effects of lower premolar extraction on posttreatment stability one year following fixed orthodontic treatment with passive self-ligating brackets (Damon system, Ormco, Orange, CA, USA). Methods: All patients were treated with fixed orthodontic appliances using passive self-ligating brackets (Damon). For retention, removable Hawley retainers were used. Two groups of patients were included in the study. Each group consisted of 23 patients: group Ex consisted of 10 male and 13 female patients (13.4 ± 1.6 years old) with extraction of lower first premolars and group NonEx consisted of 11 male and 12 female patients (13.4 ± 3.9 years old) without dental extractions. The patients’ dental models and photographs were assessed at T0 (pretreatment), T1 (the end of active orthodontic treatment: 3.3 ± 1.0 years in the Ex and 2.3 ± 0.8 years in the NonEx group) and at T2 (1 year posttreatment). All lower casts were scanned and the following dental parameters were recorded and compared between the two groups: intercanine width (ICW), anterior arch width (AAW), intermolar width (IMW), Little’s irregularity index (LII) and gingival recessions. Results: An increase in ICW (group Ex: 1.20 ± 2.51 mm and group NonEx: 0.84 ± 1.48 mm) by the end of active treatment (T1; P < 0.05), as well as a relapse regarding the ICW (group Ex: −0.1 ± 0.47 mm and group NonEx: −67% ± 0.38 mm) one year post-treatment (T2) were recorded in the samples. Relapse in the non-extraction group was statistically and clinically significant, whereas ICW values remained relatively stable in the extraction group during the posttreatment period (T1–T2). The irregularity index decreased during treatment (group Ex: −8.79 ± 6.36 mm and group NonEx: −5.24 ± 2.99 mm) and relapsed in both groups (group Ex: 0.57 ± 90 mm and group NonEx: 0.27 ± 0.53). The rate of relapse in LII was correlated to the relapse rate of ICW. A reduction of IMW was recorded in the Ex group (−1.89 ± 1.82 mm) during active treatment (P < 0.05), which remained stable 1 year posttreatment. AAW increased in both groups (group Ex: 2.77 ± 1.77 mm and group NonEx: 1.77 ± 2.04 mm) throughout active treatment and remained stable at T2. Conclusion: Intergroup comparison revealed that ICW remained stable 1 year posttreatment in the Ex group, whereas high relapse of ICW was recorded in the NonEx group. Furthermore, risk of a relapse of LII appears to be higher in cases with a relapse of the ICW.