Ebru Yurdakurban

Ortodonti, günümüzde yalnızca maloklüzyonların düzeltilmesine yönelik mekanik bir tedavi yaklaşımının ötesinde; kraniyofasiyal gelişim, dentoalveoler yapı, biyomekanik, fonksiyon, hava yolu ve uzun dönem stabilitenin birlikte değerlendirildiği kapsamlı bir klinik bilim dalıdır. Bu kitap, ortodontik pratiğin farklı alanlarında güncel bilimsel kanıtlar ile klinik uygulamayı bir araya getirmeyi amaçlamaktadır. Eserde; Sınıf II maloklüzyonlarda fonksiyonel ortopedik tedavi, erken dönem anterior open bite tedavisi, mandibular tek keser çekimli ortodontik yaklaşım ve minivida destekli hızlı maksiller genişletme (MARPE) gibi farklı maloklüzyon ve iskeletsel problemlere yönelik tedavi seçenekleri ele alınmaktadır. Şeffaf plak tedavisinde biyomekanik ilkeler, dijital planlama ve klinik uygulamalar ile interproksimal mine redüksiyonunun kanıta dayalı kullanımı da güncel ortodontik tedavinin önemli bileşenleri arasında değerlendirilmektedir. Kitabın kapsamı tedavi süreciyle sınırlı kalmayarak temporomandibular bozuklukların tanısındaki güncel yaklaşımları, obstrüktif uyku apnesinde ortodontinin rolünü ve tedavi sonrasında elde edilen sonuçların korunmasında retansiyonun önemini de kapsamaktadır. Bunun yanı sıra dijital ortodonti, tedavi planlamasında bireyselleştirme, ankraj yönetimi, büyüme-gelişim döneminin tedavi zamanlamasına etkisi ve periodontal biyolojik sınırların korunması gibi konular, modern ortodontik yaklaşımın tamamlayıcı unsurları olarak ele alınmaktadır. Bilimsel kanıt ile klinik deneyimin kesiştiği bu eser; ortodonti uzmanları, diş hekimleri, araştırma görevlileri ve lisansüstü öğrenciler için güncel klinik yaklaşımları değerlendirebilecekleri kapsamlı bir kaynak olarak hazırlanmıştır. Farklı tedavi seçeneklerinin endikasyon, biyomekanik temel, klinik uygulanabilirlik ve uzun dönem sonuçlar açısından değerlendirilmesi, okuyucuya yalnızca hangi tedavinin uygulanabileceğini değil, aynı zamanda hangi klinik durumda, neden ve hangi koşullarda uygulanması gerektiğini sorgulama olanağı sunmaktadır.

The aim of this study was to evaluate professionalism attitudes among dental stu-dents across different year levels and to investigate the effect of clinical experience on the subscales of the Professionalism Assessment Scale. This cross-sectional study included 412 students enrolled in the Faculty of Dentistry at Muğla Sıtkı Koçman University. Data were collected using the Turkish version of the Professionalism Assessment Scale (PAS-TR), the validity and reliability of which have been previously established. The scale comprises three subscales: Professional Relationship and Development, Empathy–Humanism, and Responsi-bility. Internal consistency was assessed using Cronbach’s alpha coefficient. Intergroup com-parisons were performed using the Kruskal–Wallis test, followed by the Mann–Whitney U test for post hoc pairwise analyses when statistically significant differences were identified. Overall professionalism attitude scores did not differ significantly among year levels (p > 0.05). However, students in the clinical years (fourth and fifth years) demonstrated signifi-cantly higher scores in the Professional Relationship and Development (p = 0.023) and Res-ponsibility (p = 0.030) subscales compared with preclinical students. No statistically signifi-cant differences were observed for the Empathy–Humanism subscale. The overall internal consistency of the scale was high (Cronbach’s α = 0.88). Although overall professionalism attitudes remained relatively stable across year le-vels, improvements were observed in subdomains closely related to clinical experience.

Objectives: This study aimed to compare the accuracy of automated and manual digital Bolton analyses performed using two different clear aligner therapy (CAT) planning software. Materials and Methods: Digital intraoral scans of 40 patients undergoing CAT were analyzed. Mesiodistal tooth widths and Bolton ratios were measured using: (1) a manual digital method via OrthoViewer software, (2) the ClinCheck system, and (3) DentOne software employing AI-based segmentation. The anterior and total Bolton values, as well as individual tooth width measurements, were statistically compared among the three methods. The Kruskal-Wallis test was performed to compare the differences. The Mann-Whitney U test was used for the post-hoc comparison between the groups. Measurement reliability was assessed using intra- and inter-observer intraclass correlation coefficients (ICC). Results: No significant differences were observed in anterior Bolton ratios across groups (p>0.05), whereas total Bolton values showed significant differences between software and manual measurements (p<0.05). ClinCheck demonstrated smaller deviations from manual measurements compared to DentOne, particularly for maxillary molars and incisors. The largest discrepancies were observed in the posterior region. For teeth 26 and 16, the average deviations from the manual digital method were 0.56 mm and 0.47 mm for ClinCheck, and 0.84 mm and 0.87 mm for DentOne, respectively. Conclusions: While the automated Bolton analyses performed by ClinCheck and DentOne are clinically acceptable, statistically significant differences exist when compared to manual digital methods. These discrepancies are more pronounced in posterior teeth, likely due to anatomical complexity and limitations in intraoral scanning. Thus, clinicians should perform meticulous intraoral scans and consider manual verification when interpreting automated Bolton results, particularly for molars and crowded segments.

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/Objectives: Orthodontic tooth movement is a biological process involving coordinated bone resorption and formation in response to mechanical stimulation. The aim of this study was to evaluate the temporal changes in C-terminal telopeptide of type I collagen (CTX), procollagen type I N-terminal propeptide (PINP), and vitamin D-binding protein (VDBP) levels in gingival crevicular fluid (GCF) and saliva during fixed orthodontic treatment, as well as to assess the relationships among these biomarkers. Methods: The study included a total of 27 systemically and periodontally healthy individuals comprising 14 males and 13 females. Clinical periodontal parameters were assessed at three time points: before treatment (T0), at 24–48 h (T1), and on day 40 (T2). GCF and saliva samples were collected at the same time points. Levels of CTX, PINP and VDBP in GCF and saliva were quantified using enzyme-linked immunosorbent assay. The data were analyzed using both parametric and non-parametric statistical tests. Temporal changes across the three time points were evaluated using mixed-effects models, differences between GCF and saliva biomarker levels were assessed using paired tests, and correlations were examined using Spearman correlation analysis. Results: GCF and salivary CTX levels demonstrated a significant increase from T0 to T1, while PINP levels exhibited a substantial rise from T1 to T2 (p < 0.001). Levels of VDBP in both GCF and saliva did not demonstrate significant temporal changes (p > 0.05). Higher VDBP levels in both fluids were found to be negatively associated with increases in CTX and positively associated with increases in PINP (p < 0.05). Furthermore, salivary CTX and VDBP levels exhibited a consistent increase compared to those measured in GCF at all time points (p < 0.05). Conclusions: Fixed orthodontic forces elicit sequential resorptive and formative responses in both GCF and saliva. The potential of VDBP to function as a local modulator is indicated, with the capacity to influence the balance between osteoclastic and osteoblastic activity. The evaluation of these biomarkers in non-invasive biological samples may offer a valuable approach for monitoring bone metabolism throughout orthodontic treatment.

Objective: The aim of this study was to develop convolutional neural network (CNN)-based systems to diagnose calculus, plaque, gingival hyperplasia and gingival inflammation in intraoral images from orthodontic patients. Materials and methods: A dataset of 1,000 lateral and frontal intraoral images from orthodontic patients was used to develop CNN-based models. Periodontology specialists annotated areas of dental calculus, plaque, gingival inflammation, and gingival hyperplasia on the teeth and gingiva. The dataset was divided into training (80%), validation (10%), and test (10%) sets for model development. The YOLOv8 and hybrid U-Net + ResNet50 models were examined. Their performance was evaluated on the basis of accuracy, precision, recall, F ₁ score, Tversky loss, intersection over union, mean average precision, Dice coefficient, and Cohen’s kappa. Results: The mean classification accuracy was 0.96 for the YOLOv8 model and 0.93 for the U-Net + ResNet50 model. On the basis of the Dice coefficient, the models performed best in detecting gingival hyperplasia (YOLOv8: 0.78, U-Net + ResNet50: 0.79) and worst in detecting dental calculus (YOLOv8:0.48, U-Net + ResNet50:0.53). Cohen’s kappa coefficient was highest for classifying gingival hyperplasia (YOLOv8: 0.785, U-Net + ResNet50: 0.790). The precision exceeded 0.72 across all the classifications, with the greatest precision in classifying gingival inflammation. Conclusion: Deep learning-based systems can serve as decision support tools by offering rapid and objective evaluations of dental calculus, plaque, gingival inflammation, and gingival hyperplasia. Nonetheless, the definitive diagnostic conclusion should be based on the clinician’s specialized expertise and professional judgment. Clinical relevance: The integration of CNN-based diagnostic models into clinical workflows has the potential to facilitate early periodontal diagnosis and improve accessibility to periodontal assessments in orthodontic patients.

Objective: To evaluate the accuracy between hollow and solid dental models produced using a StereoLithography Apparatus (SLA), Digital Light Processing (DLP), and PolyJet 3D printing technologies. Materials and methods: Hollow (of 1 mm, 2 mm, 3 mm shell thicknesses) and solid maxillary models were produced using SLA, DLP, and PolyJet printers. To determine the accuracy of the tested models and deviations from the reference models, 3D digital superimposition was performed. For a detailed analysis, the dental arch was subdivided into five regions which yielded root mean square (RMS) values post-registration. Six different RMS values were generated, one for the total dental arch and one for each of the five individual regions. One-Way ANOVA analysis was applied for intergroup comparisons, and post hoc comparisons were conducted using the Tukey test. The significance of the deviation of RMS values from zero was evaluated through the one-sample t test. Results: The PolyJet printer produced models with the least deviation for the total arch, while the SLA printer showed the greatest deviation. The DLP printer produced models with the least deviation for the hollow designs in the anterior region, while the SLA printer produced models with the least deviation of the solid design. The PolyJet printer showed the least deviation for both hollow and solid designs of 2 mm and 3 mm shell thicknesses in the molar regions. Except for the 1 mm shell thickness hollow design on the right side, the PolyJet printer showed the highest accuracy in the premolar-canine regions. Conclusion: Accuracy varies in the posterior and anterior regions of the dental arch as a result of different shell thicknesses produced by 3D printing technologies. The clinician should select a design that is appropriate for the intended 3D printing technology based on use and required accuracy.