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    Assessment of Mandibular Trabecular Bone Structure in Hypodivergent Growth Patterns Using Fractal Analysis
    (Mdpi, 2025) Boztas Demir, Gizem; Dogrugoren, Ruveyda; Topsakal, Kubra Gulnur; Duran, Gokhan Serhat; Gorgulu, Serkan
    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.
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    Camera-Based Monocular Depth Estimation in Orthodontics: Vision Transformer vs. CNN Model Performance
    (Mdpi, 2025) Arisan, Arda; Duran, Gokhan Serhat
    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.
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    Chatbot content analysis of patient information on orthodontic tooth extractions
    (Elsevier, 2026) Gokmen, Sule; Yurdakurban, Ebru; Topsakal, Kubra Gulnur; Duran, Gokhan Serhat
    Introduction: This study aims to evaluate the quality, accuracy, readability, and understandability of patient information provided by various Artificial intelligence (AI)-based chatbots regarding orthodontic tooth extractions Materials and methods: Two researchers created a list of questions for patients to ask the chatbots. The questions were categorized into 'Pre-extraction' and 'Post-extraction', with 20 questions in each category. Four different criteria were used to evaluate the chatbot responses to 40 questions: the Global Quality Scale (GQS), the Simple Measure of Gobbledygook (SMOG), and the Understandability and Accuracy Index. Jamovi (The Jamovi Project, 2022, version 2.3; Sydney, Australia) software was used for all statistical analyses. Results: The highest mean values were observed in Claude 3.5 sonnet for GQS, Readability, and Accuracy Index. In terms of readability, as measured by the SMOG index, all three AI-based chatbots required a college-level education for comprehension. In the 'Pre-extraction' and 'Post-extraction' sections, Claude 3.5 Sonnet demonstrated the highest mean values for the GQS, readability, and accuracy indices. In terms of Understandability subcriteria 1 and 2, statistically significant differences were observed among the three chatbots, primarily due to the variation between Gemini and Claude 3.5 Sonnet. Conclusion: AI-based chatbots with a variety of features have generally provided answers of high quality, reliability, and difficult readability to questions. Although the medical information related to orthodontic tooth extraction supplied by chatbots is of higher quality, it is recommended that individuals consult their healthcare professionals on this issue.
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    Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility
    (Mdpi, 2025) Arisan, Arda; Genc, Celal; Duran, Gokhan Serhat
    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.
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    Post-curing protocols and dimensional accuracy of 3D-printed resin materials
    (Taylor & Francis Ltd, 2025) Aksoy, Merve; Topsakal, Kubra Gulnur; Sukut, Yagizalp; Duran, Gokhan Serhat
    PurposeThe study aims to evaluate the influence of various post-curing times and temperatures on the dimensional accuracy of 3D-printed resin space maintainers.Materials and MethodsUsing biocompatible printing resins, the study designed band samples of space maintainers, which were 3D printed with the SLA printer. Samples were grouped based on thickness (0.5 mm and 1.0 mm) and post-cured under different conditions of temperature and time (60 degrees C-30 minutes, 60 degrees C-90 minutes, 60 degrees C-120 minutes, 60 degrees C-60 minutes, 40 degrees C-60 minutes, 80 degrees C-60 minutes). The accuracy of the band part of the printed maintainers was analysed using the total and 4 comparison points' arithmetic mean of the surface deviation on the band's surface.ResultsThe study revealed that the post-curing conditions notably influenced the dimensional accuracy of the printed samples. Post-curing temperature and duration variations produced significant differences in dimensional stability between the groups. Particularly, a post-curing period of 60 degrees C for 60 minutes was identified as optimal for achieving better dimensional accuracy, especially for samples with 1.0 mm thickness.ConclusionsDifferent post-curing parameters significantly affect the dimensional accuracy of 3D-printed resin space maintainers. The 1.0 mm band thickness and a 60 degrees C and 60 minutes post-curing protocol optimize the accuracy of the maintainers.
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    Shear bond strength of 3-dimensional-printed orthodontic brackets
    (Mosby-Elsevier, 2025) Duran, Gokhan Serhat; Topsakal, Kubra Gulnur; Akgun, Yusufcan; Bakirhan, Nurgul Karadas; Gorgulu, Serkan
    Introduction: This in vitro study aimed to evaluate the shear bond strength (SBS) and adhesive remnant index (ARI) of 3-dimensional-printed orthodontic brackets with 3 different base designs and to examine the surface morphologic differences of remaining adhesives using scanning electron microscopy (SEM). Methods: Sixty maxillary premolar teeth (n = 20 per group) were randomly allocated into 3 bracket base design groups: (1) custom, (2) macroretentive, and (3) standard. After digital scanning of the teeth, the brackets were printed using a MAX UV DLP 3D printer (Asiga, Sydney, Australia). Each bracket was bonded using Transbond XT composite and light-cured for 10 seconds with a light-emitting diode curing unit (Valo; Ultradent, South Jordan, Utah). Subsequently, SBS testing was performed using a universal testing machine at a 1 mm/min crosshead speed, and fracture loads were recorded in megapascals (MPa). After debonding, ARI scores were evaluated under a digital microscope by a blinded investigator and reevaluated after 2 weeks for reliability. In addition, bracket bases and tooth surfaces were examined under a high-resolution SEM (30 kV, 20 mm working distance). Results: The highest SBS values were observed in the custom base design group (group A, 8.05 +/- 4.69 MPa), followed by the macroretentive group (group B, 6.31 +/- 3.80 MPa) and the standard group (group C, 5.91 +/- 6.09 MPa). The differences between groups A and C were statistically significant (P = 0.017). ARI scores revealed that in groups A and B, most adhesive remained on the tooth (ARI score 2 predominated), whereas group C demonstrated a more variable ARI distribution. In addition, according to the SEM results, most adhesives have remained on the teeth in all groups. Conclusions: Custom mesh designs in 3D-printed orthodontic brackets significantly enhance bond strength and adhesive retention, making them a promising option for clinical use. Future studies should investigate their performance under conditions that simulate the oral environment to validate their clinical applicability. (Am J Orthod Dentofacial Orthop 2025;168:379-86)