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Aim The aim of this study was to evaluate the permanent maxillary first molar rotation (PMMR) angles in cases of maxillary transverse skeletal deficiency (MTSD) and to compare them with a control group. Materials and methods In this study, which included a total of 88 patients (50 females, 38 males, with a mean age of 14.98 ± 2.14 years), consisting of 66 patients with MTSD and 22 patients in the control group, four groups were divided: Group 1 (MTSD without molar crossbite), Group 2 (MTSD with bilateral molar crossbite), Group 3 (MTSD with unilateral (right-sided) molar crossbite), and Group 4 (Control Group). Skeletal deficiencies were evaluated by measuring the interjugular, jugale right (JR), and jugale left (JL) distances on posteroanterior cephalograms (PACs), while occlusal relationships were assessed using 3-dimensional (3D) intraoral models. PMMR angles were measured using the 3D Slicer software on 3D intraoral models with the Ricketts Molar-Cusp Reference Line and the midsagittal reference plane. Statistical significance was set at p  < 0.05. Results The PMMR angles and JR and JL distances of Group 2 were significantly higher, while the interjugular distance was found to be the lowest ( p  < 0.05). There was no significant difference in the PMMR angles between the Control group and Group 1 ( p  > 0.05), while the JR and JL distances were significantly smaller in the Control group ( p  < 0.05). In Group 3, on the crossbite side, both the PMMR and the JR and JL distances were significantly higher than on the non-crossbite side ( p  < 0.05). A significant positive correlation was found between PMMR angles and JR and JL distances ( p  < 0.05). Conclusion It was concluded that (i) mesiopalatal PMMRs are observed in the MTSDs with molar crossbite, (ii) molars with normal molar occlusal relationships have normal PMMR angles even in the presence of MTSDs, and (iii) early detection of MTSD enables timely interventions, preventing treatment delays and improving occlusal outcomes, particularly in developing patients, thereby optimizing long-term orthodontic results. Clinical relevance Considering the differences in PMMR angles between MTSD patients with and without molar crossbite, these findings should be taken into account when designing expansion appliances to achieve molar derotation in these patients.

Objectives: The aim of this study was to compare the responses of large language models (LLMs)—DeepSeek V3, GPT 5, and Gemini 2.5 Flash—to patients’ frequently asked questions (FAQs) regarding root canal treatment in terms of accuracy and comprehensiveness, and to assess the potential roles of these models in patient education and health literacy. Methods: A total of 37 open-ended FAQs, compiled from American Association of Endodontists (AAE) patient education materials and online resources, were presented to three LLMs. Responses were evaluated by expert clinicians on a 5-point Likert scale for accuracy and comprehensiveness. Inter-rater and test–retest reliability were assessed using intraclass correlation coefficients (ICCs). Differences among models were analyzed with the Kruskal–Wallis H test, followed by pairwise Mann–Whitney U tests with effect sizes (Cliff’s delta, δ). A p-value < 0.05 was considered statistically significant. Results: Inter-rater agreement was excellent, with ICCs of 0.92 for accuracy and 0.91 for comprehensiveness. Test–retest reliability also demonstrated high consistency (ICCs of 0.90 for accuracy and 0.89 for comprehensiveness). DeepSeek V3 achieved the highest scores, with a mean accuracy of 4.81 ± 0.39 and a mean comprehensiveness of 4.78 ± 0.41, demonstrating statistically superior performance compared to GPT 5 (accuracy 4.0 ± 0.0; comprehensiveness 4.05 ± 0.4; p < 0.05, δ = 0.81 for accuracy, δ = 0.69 for comprehensiveness) and Gemini 2.5 Flash (accuracy 3.83 ± 0.68; comprehensiveness 3.81 ± 0.7; p < 0.05, δ = 0.71 for accuracy, δ = 0.70 for comprehensiveness). No significant difference was observed between GPT 5 and Gemini 2.5 Flash for either accuracy (p = 0.109, δ = 0.16) or comprehensiveness (p = 0.058, δ = 0.21). Conclusions: LLMs, such as DeepSeek V3, which can provide satisfactory responses to FAQs may serve as valuable supportive tools in patient education and health literacy; however, expert clinician oversight remains essential in clinical decision-making and treatment planning. When used appropriately, LLMs can enhance patient awareness and support satisfaction throughout the root canal treatment.

(1) Background and Objectives: Congenitally missing bilateral maxillary lateral incisors (CMBMLIs) present significant aesthetic, functional, and psychosocial challenges that require an orthodontic approach based on multidisciplinary consensus. The aim of this study was to evaluate the skeletal, dental, and soft tissue changes in patients with CMBMLIs treated with space opening and closure methods and to compare these changes with those in untreated individuals. (2) Materials and Methods: A total of 53 patients (mean age 16 ± 3.5 years) were included, and three groups were formed: the study groups, consisting of the space opening group (n = 18) and the space closure group (n = 17), and the control group (n = 18), which had ideal occlusion. A total of 14 angular and 13 linear measurements were performed on lateral cephalograms before (T0) and after (T1) treatment. Statistical significance was set at p < 0.05. (3) Results: Compared to the control group, significant post-treatment changes were more evident in dental measurements and less evident in skeletal and soft tissue measurements. A statistically significant increase in the U1/SN angle was observed in the space opening group compared to the space closure group. The U1/NA angle increased significantly in both study groups, with a greater increase in the space opening group. However, although the change in U1/NA angle was not significantly different between groups, the increase was greater in the space opening group. No significant differences were found between the control and study groups in the nasolabial angle, upper lip length and thickness, and the distance from the upper and lower lips to the E-line. (4) Conclusions: While space opening and closure methods had minimal effects on most skeletal and soft tissue parameters, the space opening method significantly altered the maxillary incisor position. Considering the waiting period for prosthetic restoration after space opening and potential alveolar bone limitations, space closure is recommended for CMBMLIs when feasible because it ensures a more predictable planned maxillary incisor position.

: The aim of this study was to evaluate the accuracy of aesthetic assessments performed by artificial intelligence (AI)-based large language models (LLMs) using the Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN-AC), which is widely applied to determine the need for orthodontic treatment. : A total of 150 frontal intraoral photographs from patients in the permanent dentition, scored from 1 to 10 on the IOTN-AC, were assessed by two AI-based LLMs (ChatGPT-5 and ChatGPT-5 Pro). Two experienced clinicians independently scored all photographs, with one evaluator's scores used as the reference (κ = 0.91, ICC = 0.88). Model performance was analyzed by comparing IOTN-AC scores and treatment need classifications. In addition, performance parameters such as accuracy, precision, specificity, and sensitivity were evaluated. Statistical analyses included Spearman correlation, Cohen's Kappa, ICC, Mean Absolute Error (MAE), Wilcoxon signed-rank test, and Bland-Altman analysis. : Both models demonstrated positive and significant correlations with the reference values for scoring and classification ( < 0.001). Compared to GPT-5 Pro, the GPT-5 model exhibited superior performance, with a lower error rate (MAE = 1.47) and higher classification accuracy (66.7%). Bland-Altman analysis showed that most predictions fell within the 99% confidence interval, and regression analysis revealed no systematic bias ( > 0.05). Conversely, the models failed to achieve consistently high performance in each of the performance parameters. : The findings revealed that although AI-based LLMs are promising, statistical accuracy alone is insufficient for safe clinical use, and they should demonstrate consistently high performance across all parameters.

Background and aim Periodontal health is a critical factor in the development of gingival recession, which may be influenced by orthodontic treatment and various patient-related factors. The aim of this study was to evaluate the prevalence of gingival recession observed during the retention phase after orthodontic treatment and the contributing etiological factors. Materials and methods A total of 96 patients (65 females, 31 males; mean age 20.39 ± 2.21 years) were included in the study during routine follow-up examinations in the retention phase, at least six months after the completion of non-extraction fixed orthodontic treatment and their sociodemographic data, oral hygiene habits, and clinical periodontal measurements were evaluated. The relationships between dentoalveolar cephalometric measurements obtained from lateral cephalograms and gingival recession and gingival phenotype were evaluated. The normality of the data was assessed using the Kolmogorov-Smirnov test, and Mann-Whitney U and Chi-square tests were applied for continuous and categorical variables, respectively. Logistic regression analyses were performed to evaluate risk factors. Statistical significance was considered as p  < 0.05. Results Gingival recession was found to be more prevalent in thin gingival phenotypes compared to thick phenotypes and was observed to be more pronounced when bleeding on probing was 30% and higher ( p  < 0.05). It was observed that gingival recession increased with age ( p  < 0.05). No statistically significant difference was found between gingival recession and other periodontal clinical measurements, sociodemographic data, and oral hygiene habits ( p  > 0.05). No statistically significant relationship was observed between lower incisor protrusion and gingival recession ( p  > 0.05). Conclusions It was concluded that (i) gingival phenotype, bleeding percentage on probing, and age had a considerable effect on gingival recession, whereas orthodontic tooth movement had no significant effect, and (ii) after orthodontic treatment, despite the achievement of a well-aligned teeth and dental arch, the frequency of periodontal check-ups should be increased to reduce the risk of gingival recession. Clinical relavance Orthodontic treatment, consider periodontal conditions related to gingival recession, like thin phenotype and bleeding on probing.

Impacted canines, which play an important role in smile aesthetics and functional occlusion, can lead to dental and skeletal malocclusions. In this study the aim was to evaluate the relationship between impacted maxillary canines and malocclusion. A total of 151 patients comprising 101 females and 50 males aged between 13 and 33 years were included. The groups were divided based on age, gender, skeletal and dental classification, and sector classification. Angular and linear measurements were performed on lateral cephalometric and panoramic radiographs. In panoramic radiographs, the vertical distance of the impacted canine to the occlusal plane and the angle between it and the bicondylar plane were measured and sector classification was performed according to its relationship with the root of the lateral incisor. Skeletal classification was performed according to the ANB angle on lateral cephalometric radiographs and dental classification by molar relationship via the intraoral photographs. The Chi-square test analyzed independent qualitative and quantitative data using Kruskal–Wallis and Man–Whitney U tests. The statistical significance level was accepted as p < 0.05. According to the intraclass correlation test, an excellent positive correlation was found with 0.985 for canine distance and 0.993 for canine angle between the repeated measurements. The impaction of the maxillary right canine was significantly highest in females and lowest in males. The impacted canine angle was significantly highest in sector 1 and lowest in sector 4. Distance to the occlusal plane was significantly higher in dental Class II and sector 4. It was observed that there was a considerable relationship between impacted maxillary canines and malocclusion; bilateral impacted canines were more frequent in skeletal Class III, and the distance of impacted canines to the occlusal plane increased while their angles decreased both in dental Class II and from sectors 1 to 4.

Background/Aims: The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. Methods: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand–wrist radiographs (30 from each growth stage—pre-peak, peak, and post-peak—with equal male and female distribution). Reference standards were ensured by expert orthodontists using Fishman’s Skeletal Maturity Indicators (SMI) system and the Greulich–Pyle Atlas, with each radiograph analyzed by three GPT models using standardized prompts. Model performances were evaluated through statistical analyses assessing agreement and prediction accuracy. Results: All models showed significant agreement with the reference values in bone age prediction (p < 0.001), with GPT-o1-pro having the highest concordance (Pearson r = 0.546). No statistically significant difference was observed in the mean absolute error (MAE) among the models (p > 0.05). The GPT-o4-mini-high model achieved an accuracy rate of 72.2% within a ±2 year deviation range for bone age prediction. The GPT-o1-pro and GPT-o4-mini-high models showed bias in the Bland–Altman analysis of bone age predictions; however, GPT-o1-pro yielded more reliable predictions with narrower limits of agreement. In terms of growth stage classification, the GPT-4o model achieved the highest agreement with the reference values (κ = 0.283, p < 0.001). Conclusions: This study shows that general-purpose GPT models can support bone age and growth stages prediction, with each model having distinct strengths. While GPT models do not replace clinical examination, their contextual reasoning and ability to perform preliminary assessments without domain-specific training make them promising tools, though further development is needed.

(1) Background and aim: The effects of functional therapies on dentoalveolar and skeletal structures have been investigated in orthodontics for many years. The aim of this retrospective study was to evaluate the changes caused by fixed and removable functional therapy in the mandibular anterior trabecular structures using fractal dimension (FD) analysis. (2) Methods: A total of 60 patients with skeletal and dental class II malocclusion were included in the study and three groups were formed: the untreated control group (CG), the Forsus fatigue-resistant device group (FFRDG), and the Monoblock group (MBG). Bone areas of interest determined in the buccoapical of the mandibular incisors and the symphysis in the lateral cephalometric radiographs taken before (T0) and after (T1) functional therapy were evaluated using FD analysis. The relationship between the FD and IMPA (Incisor Mandibular Plane Angle) angles was evaluated. Parametric and nonparametric tests were used in statistical analysis according to normality distribution. The statistical significance level was determined as p < 0.05. (3) Results: There was no statistically significant difference between the FD values of all groups at T0 (p > 0.05). At T1, buccoapical FD values were significantly lower in FFRDG and MBG compared to the control group (p < 0.05), while symphyseal FD values were not found to be significant (p > 0.05). The IMPA angle was significantly lower in the FFRDG and MBG than in the control group at T0, while it was higher at T1 (p < 0.05). While a significant negative correlation was observed between the IMPA angle and buccoapical FD values in both FFRDG and MBG (p < 0.05), it was not observed with the symphysis FD values (p > 0.05). (4) Conclusions: Trabecular changes caused by functional therapy in the mandibular anterior bone can be evaluated on lateral cephalometric radiographs with FD analysis. It was concluded that orthodontists should ensure controlled changes in the IMPA angle during functional therapy, especially for the decreases in FDs seen in the buccoapical alveolar region due to the forward movement of the mandibular incisors.

Background This study aimed to evaluate the relationship between maxillary sinus volume (MSV) and alveolar bone trabeculation using fractal dimension (FD) analysis across different vertical skeletal patterns, and to assess its potential implications for orthodontic mini-implant (OMI) stability. Methods CBCT images of 84 skeletal Class I individuals (47 females, 37 males; mean age 20.08 ± 2.25 years) were analyzed and categorized into three vertical skeletal groups (28 normodivergent, 28 hyperdivergent, 28 hypodivergent). MSVs were measured bilaterally using 3D Slicer, and FD analyses were conducted on alveolar bone regions of interest (ROIs) located between the roots of teeth #14–15, #15–16, #16–17, and #24–25, #25–26, #26–27. MSV and FD values were analyzed within and between groups using one-way ANOVA, Kruskal–Wallis test, paired t-tests, Wilcoxon signed-rank tests, and Spearman’s correlation test at a significance level of p  < 0.05. The effects on OMI stability were then evaluated. Results Both right and left MSVs, as well as FD values in the ROIs between teeth #14–15, #15–16, #24–25, and #25–26, were found to be significantly higher in hypodivergent individuals. The posterior increase in FD values was significant only in the normodivergent group. In hypodivergent individuals, right MSVs were significantly correlated with FD values at the 14–15 ROI, and left MSVs with those at the 24–25 ROI, while no such correlation was found in other vertical patterns. Conclusions In hypodivergent individuals, increased trabecular complexity between the maxillary second premolar and first molar may enhance OMI stability; however, the MSV, which correlates significantly with FD values in this region, necessitates caution due to the potential risk of sinus perforation during insertion.

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.