Gökhan Serhat Duran

This study combined thermomechanical characterisation with finite element analysis to compare polyethylene terephthalate glycol (PET-G) and Tera Harz TC-85 orthodontic aligner materials. Tensile properties were evaluated at 23, 37, 55 and 80 °C, with five observations per material and temperature. Identical full-arch geometries with a nominal thickness of 0.75 mm were subjected to 600 N compression at 300 K, heating under maintained load to 352 K and cooling to 310 K. At 23 °C, tensile moduli were 1396.4 ± 44.9 MPa for PET-G and 675.4 ± 21.2 MPa for TC-85. Dynamic mechanical analysis (DMA) loss-tangent maxima occurred at sampled temperatures of 80 and 45 °C. Tensile and DMA moduli diverged at elevated temperatures. Initial maximum effective strains were 0.165 and 0.196, and medium-to-fine mesh changes were 1.78% and 2.61%. TC-85 showed a greater reduction in maximum strain following heating to 78.85 °C. Recovery benchmark values at 60 min were 63.3% for PET-G and 92.5% for TC-85; paired-series metrics quantified curve agreement. PET-G exhibited higher tensile stiffness, whereas TC-85 exhibited greater benchmark recovery and a larger thermal-stage strain reduction. The high-temperature endpoint represents ex vivo activation. Constitutive and experimental limitations constrain extrapolation to clinical appliance performance.

Objectives: To develop and validate an automated pipeline for maxillary dental midline localization on posed smile photographs and compare the performance of four convolutional neural network (CNN) backbones. Materials and Methods: A total of 221 standardized frontal posed smile photographs were collected. The maxillary dental midline was annotated by two orthodontists across two sessions, and mean coordinates served as the orthodontist-derived reference standard. An automated pipeline including face detection, interpupillary-line rotational normalization, mouth region extraction, and CNN-based keypoint regression was developed. Four CNN models (EfficientNet-B0, ResNet-18, MobileNetV3-Small, and HRNet-W18) were trained and compared using 5-fold stratified cross-validation. Deviation was expressed as the angle between the predicted dental midline and the interpupillary-derived photographic reference line (T-line). Results: HRNet-W18 achieved the lowest mean angular error (0.63° ± 0.63°) and the highest ≤1° error-threshold accuracy (81.0%). All 4 models exceeded 90% accuracy at the ≤2° threshold. No significant effect of tooth visibility grade on angular error was observed. HRNet-W18 also showed the highest agreement with the reference standard [intraclass correlation coefficient (ICC) = 0.721] and minimal Bland–Altman bias (+0.03°). Post hoc pairwise comparisons showed that HRNet-W18 outperformed all other models across all error metrics. Limitations: The sample was limited to standardized posed smile photographs from a single center, and generalizability to diverse populations and less controlled conditions remains to be established. Conclusions and Implications: The automated pipeline showed high accuracy for dental midline localization on posed smile photographs relative to an orthodontist-derived reference standard. Further validation on larger and more diverse datasets is needed before clinical application.

This study evaluated the resistance under load of a novel monolithic prosthetic design integrating functional orthodontic components within a digitally fabricated framework. Sixty-six specimens were allocated into three groups: (1) a Design Group consisting of one-piece 3D-printed customized metal copings with integrated brackets or tubes; (2) a Porcelain Crown Group with conventionally bonded orthodontic attachments; and (3) a Natural Teeth Group with brackets and tubes bonded to extracted human teeth. Each group included premolar (bracket) and molar (tube) subgroups (n = 11). All specimens were subjected to shear loading using a universal testing machine. Higher resistance values were observed in the monolithic group (92.56 ± 63.88 MPa) (p < 0.001); however, these values represent structural resistance rather than shear bond strength. Despite the wide variability, all measured values remained above the clinically accepted threshold. No statistically significant differences were observed between porcelain crowns and natural teeth in premolar or molar subgroups. The findings indicate that eliminating the adhesive interface enhances structural integrity under shear forces. This monolithic orthodontic–prosthetic approach may provide a clinically relevant alternative in cases where conventional bonding is not feasible and supports a fully digital, patient-specific workflow through scanner library integration.

Background: This study evaluates the accuracy and repeatability of 3D Gaussian Splatting (3DGS)‐generated 3D facial models by comparing anthropometric measurements derived from these models with those obtained using conventional 2D photogrammetry and direct anthropometry. Methods: In this cross‐sectional study, 58 adults (18–25 years) underwent direct anthropometry, standardised 2D photography with calibration, and 3DGS facial reconstruction using a smartphone‐based workflow. A predefined set of linear and angular facial measurements was recorded; each measurement was repeated three times by the same observer. Between‐method comparisons were performed using paired tests as appropriate. Pearson correlation coefficients were reported to describe association, whereas agreement and potential interchangeability were evaluated primarily using Bland–Altman bias and 95% limits of agreement (LoA); plots were additionally inspected for proportional bias and influential outliers. Results: 3DGS demonstrated small mean differences relative to direct anthropometry for several measurements, with generally narrower LoA than 2D photogrammetry. Correlations were typically higher for 3DGS than for 2D photogrammetry; however, Bland–Altman analyses indicated that agreement varied by variable. For selected measurements, LoA were wide (reaching approximately 10–18 mm for some linear outcomes and exceeding 20° for certain angular outcomes), indicating limited interchangeability for individual‐level clinical decisions. Perioral measures showed greater dispersion for 2D photogrammetry, particularly for N–Stm, and a small number of observations fell outside the 95% LoA in ANT vs. 2D comparisons, consistent with occasional landmark identification or reconstruction‐related variability. Conclusion: 3D Gaussian Splatting (3DGS) may offer a useful alternative workflow for selected facial analysis applications by enabling the generation of measurable three‐dimensional facial models, from which anthropometric variables can subsequently be derived. Although 2D photogrammetry is readily accessible and practical to implement, its inherent limitation in representing three‐dimensional anatomy may compromise measurement accuracy in certain clinical contexts. Overall, these findings indicate that 3DGS could support orthodontic practice and research as a 3D model‐generation technique; however, further validation across diverse patient groups, acquisition conditions, expanded landmark‐based geometric error metrics, and additional outcome measures is warranted before routine clinical adoption can be recommended.

Arda Arısan, Celal Genç, Gökhan Serhat Duran. Korean J Orthod 2026;56:207-20. https://doi.org/10.4041/kjod25.320

Background: The purpose of this study was to compare the angle of the maxillary central incisors in Class I and Class III malocclusions, with cephalometric radiography Methods: This study analyzed 120 lateral cephalometric radiographs classified into study and control groups based on Angle’s classification, ANB angles, and Wits appraisal. The study group with skeletal Class III malocclusion, while the control group included individuals with skeletal Class I malocclusion The study group comprised 33 female and 27 male patients with a mean age of 13,59± 3,53, while the control group included 32 female and 28 male patients with a mean age of 14,26±2,60. The collum angle was defined as the angle between the long axes of the root and crown. Various cephalometric parameters were assessed, and descriptive statistics were calculated. Results: The study group exhibited significantly greater proclination of the maxillary incisors than the control group, as evidenced by higher 1/NA° and 1-NA (mm) values (p < 0.001). The mean collum angle was significantly higher in Class III individuals (4.05° ± 3.98) than in Class I individuals (−0.44° ± 5.24) (p<0.001), indicating a more pronounced labial root positioning. These morphological features are consistent with true skeletal Class III malocclusion. Conclusion: The increased collum angle observed in Class III individuals results in a more labial root position, which may heighten the risk of dehiscence and fenestration during orthodontic treatment. Therefore, clinicians should consider this factor when planning treatment strategies.

Background: Multimodal large language models (LLMs) are increasingly being explored as clinical support tools, yet their capacity for orthodontic biomechanical reasoning has not been systematically evaluated. This retrospective study assessed their ability to analyze treatment mechanics and explored their potential role in supporting orthodontic decision-making. Methods: Five publicly available models (GPT-o3, Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4.0, and Grok) analyzed 56 standardized intraoral photographs illustrating a diverse range of active orthodontic force systems commonly encountered in clinical practice. Three experienced orthodontists independently scored the outputs across four domains—observation, interpretation, biomechanics, and confidence—using a 5-point scale. Inter-rater agreement and consistency were assessed, and statistical comparisons were made between models. Results: GPT-o3 achieved the highest composite score (3.34/5.00; 66.8%), significantly outperforming all other models. The performance ranking was followed by Claude (57.8%), Gemini (52.6%), GPT-4.0 (48.8%), and Grok (38.8%). Inter-rater reliability among the expert evaluators was excellent, with ICC values ranging from 0.786 (Confidence Evaluation) to 0.802 (Observation). Model self-reported confidence showed poor calibration against expert-rated output quality. Conclusions: Multimodal LLMs show emerging potential for assisting orthodontic biomechanical assessment. With expert-guided validation, these models may contribute meaningfully to clinical decision support across diverse biomechanical scenarios encountered in routine orthodontic care.

Background: Monocular Depth Estimation (MDE) is a computer vision approach that predicts spatial depth information from a single two-dimensional image. In orthodontics, where facial soft-tissue evaluation is integral to diagnosis and treatment planning, such methods offer new possibilities for obtaining sagittal profile information from standard frontal photographs. This study aimed to determine whether MDE can extract clinically meaningful information for facial profile assessment. Methods: Standardized frontal photographs and lateral cephalometric radiographs from 82 adult patients (48 Class I, 28 Class II, 6 Class III) were retrospectively analyzed. Three clinically relevant soft-tissue landmarks—Upper Lip Anterior (ULA), Lower Lip Anterior (LLA), and Soft Tissue Pogonion (Pog′)—were annotated on frontal photographs, while true vertical line (TVL) analysis from cephalograms served as the reference standard. For each case, anteroposterior (AP) relationships among the three landmarks were represented as ordinal rankings based on predicted depth values, and accuracy was defined as complete agreement between model-derived and reference rankings. Depth maps were generated using one vision transformer model (DPT-Large) and two CNN-based models (DepthAnything-v2 and ZoeDepth). Model performance was evaluated using accuracy, 95% confidence intervals, and effect size measures. Results: The transformer-based DPT-Large achieved clinically acceptable accuracy in 92.7% of cases (76/82; 95% CI: 84.8–97.3), significantly outperforming the CNN-based models DepthAnything-v2 (9.8%) and ZoeDepth (4.9%), both of which performed below the theoretical chance level (16.7%). Conclusions: Vision transformer-based Monocular Depth Estimation demonstrates the potential for clinically meaningful soft-tissue profiling from frontal photographs, suggesting that depth information derived from two-dimensional images may serve as a supportive tool for facial profile evaluation. These findings provide a foundation for future studies exploring the integration of depth-based analysis into digital orthodontic diagnostics.

The objective of this study is to evaluate the trabecular structure in hypodivergent individuals using fractal analysis, with a particular focus on specific mandibular regions. This study aims to assess the impact of hypodivergent growth patterns on bone microarchitecture. This research involved a methodological approach using panoramic radiographs to assess trabecular structure at specific regions of the mandible using fractal analyses. The dimensions of the fractals were calculated with the use of the box-counting technique by the software Image J (v1.53c; Bethesda, MD, USA, National Institutes of Health), while the statistical evaluations were carried out with the Jamovi Software (The Jamovi Project, version 2.3.21.0). The study found significant differences in fractal dimension values between hypodivergent individuals and the control group, particularly in the condyle and angulus regions, indicating a less complex trabecular structure in hypodivergent individuals. This study concludes that individuals with a hypodivergent growth pattern exhibit alterations in trabecular bone structure within the mandibular condyle and angulus regions, characterized by reduced complexity. These findings suggest that increased occlusal forces and mechanical stress associated with this growth pattern may contribute to changes in trabecular architecture. Understanding these variations is essential for orthodontic and maxillofacial diagnosis, treatment planning, and biomechanical considerations, particularly in cases requiring vertical dimension management or anchorage control.