19 research outputs found
Association Between Etiological Factors and Dentin Hypersensitivity: A Cross-Sectional Study in Turkey
No full textObjective: This study purposed to estimate the prevalence of DH and how effective the etiological factors are in the development of DH in Turkey.Methods: Demographic features, hygiene habits, bruxism, beverage habits, parafunctional habits, smoking, and other medical problems were asked. Air was blasted to the teeth via the air-water syringe for 3 seconds. The Visual Analogue Scale was used to measure DH sensitivity, and data were recorded in the 0-100 mm range, and 5 mm and higher were considered DH. The attrition, abrasion, erosion, abfraction per dental type (Incisal OR Canine OR Premolar OR Molar) were recorded. The same procedure was applied for abnormal tooth position and gingival recession.Results: A total of 4476 teeth and 236 individuals were evaluated. Significantly higher DH frequency was observed in females (p= .034), the 40-55 age range (p= .009), and non-smokers (p= .016). Those who brushed their teeth three times a day or more (p .05).Conclusion: DH is a multi-etiological symptom affected by demographic attributes, hygiene, and other habits. Clinicians should painstakingly distinguish the source of DH to gain the long-term success of DH treatment, which depends on many etiological factors
Comparison of degree of conversion performance of bulk-fill resin composites: A systematic review and network meta-analysis of in vitro studies
No full textObjectives: To systematically compile data on the degree of conversion (DC) for bulk-fill composites using a network meta-analysis. Methods: A systematic search for in vitro studies of DC of bulk-fill composites was performed in PubMed, Web of Science, Scopus, and Open Grey. Risk of bias within studies and due to missing evidence was assessed using the Joanna Briggs Institute scoring system and ROB-MEN tool, respectively. The primary outcome was the DC of bulk-fill composites. Surface Under the Cumulative Ranking curve (SUCRA) was used to rank relative performance. Inconsistencies in the model were investigated to ensure its validity and the level of confidence in the network meta-analysis (CINeMA) was assessed. Results: A total of 28 studies were included in the quantitative analysis. The average DC values (%) for 0-h/top, 0h/bottom, 24-h/top, and 24-h/bottom were 59.09, 57.14, 66.73, and 63.87, respectively. According to their SUCRA ranking, the best-performing composites were: SonicFill, Venus Bulk Fill, and SDR (0-h/top), Reveal HD, i-Flow Bulk Fill, and Venus Bulk- Fill (0-h/bottom), Venus Bulk Fill, SDR, and QuiXfil (24-h/top), and Venus Bulk Fill, Aura Bulk Fill, and i-Flow Bulk Fill (24-h/bottom). Incoherence between direct and indirect evidence was identified as the most significant factor affecting confidence. Conclusions: DC values of bulk-fill composites were within the range commonly reported for previous generations of conventional composites, with flowable composites tending to perform better than sculptable composites. High variability in DC data was observed, which may be attributed to incompletely understood methodological differences. Clinical significance: DC is a fundamental parameter that influences multiple mechanical and biological properties of resin composites and is particularly relevant for the group of bulk-fill composites that are designed for use in thick layers
) on the undergraduate students in an endodontic training program and its predictive capability on complications
No full textIntroductionDental students face a number of challenges when it comes to performing root canal treatments (RCTs). The Endodontic Complexity Assessment Tool (E-CAT) was developed to assist dental practitioners in assessing the complexity of RCTs before beginning treatment. Materials and MethodsThe E-CAT was filled out independently by both the educator and the student. To allow educators to record scores and complexity classes, they transferred their and students' forms to the website . Students began endodontic treatment after learning about the complexity level of the case. The educators were responsible for recording any complications encountered in every case from the outset to 1 month after treatment. ResultsA total of 70 students, 33 in fourth and 37 in fifth-grade, were included in the study. In the cases with higher E-CAT scores, complications such as misdiagnosed, faulty access cavity, furca or coronal third perforation, insufficient root canal instrumentation, working length loss, canal blockage, overpreparation, incomplete root canal filling and overfilling were experienced significantly more often compared to the cases with lower E-CAT scores (p < .05). The number of complications (r = .40, p < .001), treatment sessions (r = .44, p < .001), and teacher support (r = .24, p < .001) positively correlated with E-CAT score (p < .05). ConclusionThe E-CAT is an effective tool for assisting dental students in understanding technical challenges, such as complex root canal anatomy and possible complications during treatment. Educators can also use e-CAT to pre-select clinical cases and standardise student training by offering cases of equal complexity
Root canal morphology of anterior permanent teeth in Jordanian population using two classification systems: a cone-beam computed tomography study
No full textAbstract Background Adequate knowledge of root canal morphology and its variation is essential for success of root canal treatment and to overcome treatemnt failure. The aim of this study was to investigate the root and canal morphology of mandibular anterior teeth using 2 classification systems. Methods 3342 lower anteriors were evaluated from 557 CBCT scans. The images were examined in sagittal, axial and coronal views using a CS 3D imaging software (V3.10.4, Carestream Dental). Demographic data recorded, the number of roots and canal’s morphology were described according to Vertucci and Ahmed classifications. Results Frequency of Type I configuration was significantly the highest in incisors and canines (76%, N = 2539), followed by Type III (20.6%, N = 687). Type II (1.1%, N = 37), IV (1.1%, N = 37), and V (0.3%, N = 11) were rarely encountered. 0.9% (N = 31) of the teeth could not be classified with the Vertucci System. The frequency of 2 roots (2MA in Ahmed classification) which has no correspondence in the Vertucci classification, was 1.1% (N = 38), it was significantly higher in canines and in females (35 canines and 3 laterals). A moderate correlation in root canal morpology was found between the left and right sides (V > 0.30). 80% (N = 2538) of the teeth did not exhibit any divergence/merging. The bifurcation level occurred mostly in the middle third of the root. Conclusions One fourth of anterior teeth had variation from the simple type I canal configuration and therefore requires attention during treatment. The new classification system offers a more accurate and simplified presentation of canal morphology. Clinical relevance The prevalence and mid root bifurcation of second canal in lower anteriors requires attention to ensure adequate quality root canal treatment without compromising the integrity of teeth
Root canal morphology of mandibular anterior permanent teeth in Turkish sub-population using two classification systems: a cone-beam computed tomography study
No full textThis study examined the root and canal morphology of mandibular anterior teeth (MA) in the Turkish sub-population using cone-beam computed tomography (CBCT), comparing the findings based on Vertucci's and Ahmed et al. classification systems. The CBCT images were acquired using the 3D Accuitomo CBCT device. Images that were deemed suitable for visualizing the roots, canals, and the complete pulp chamber and apex were included in the study. Vertucci and Ahmed et al. classification systems were employed to determine the root canal morphology. 500 CBCT images and 3000 teeth were analyzed. Type I ((1)MA(1)) was the most frequent, followed by Type III ((1)MA(1-2-1)). 3.8% of teeth could not be classified with Vertucci system. In canine teeth, Vertucci type III ((1)MA(1-2-1)) was significantly more prevalent in males than females (p = 0.038) and Type I ((1)MA(1)) was less frequent in individuals aged 41-50 (p 78%). One-third of mandibular incisor teeth have two canals, with a significant number exhibiting canal divergence/merging that was separated in the middle region and merged in the apical region. The Vertucci classification was found to be inadequate in some cases, while Ahmed et al. classification was able to classify all mandibular incisors with a single code. Ahmed et al. classification is a more useful system for classifying all MA
Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning
No full textObjectiveThis study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.Materials and methodsHigh-resolution radiographs of 200 patients aged 20-77 (41.0 +/- 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets.ResultsWhen all 12 features are used together, the accuracy rate is found to be 82.6 +/- 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 +/- 0.9%), condyle height (78.2 +/- 0.5%), and ramus height (77.2 +/- 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 +/- 0.4%.ConclusionMachine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.The present article has been sourced from Hanife Pertek's Master's dissertation
Effectivity of patch test in determining the relationship between oral lichenoid lesions and dental amalgam: A meta-analysis
No full textHatipoglu, Omer/0000-0002-4628-8551Objective: This study aimed to make a meta-analysis of studies which examined the effectivity of the patch WA to determine the relationship between lichenoid lesions and dental amalgams. Methods: Prisma statement guide was followed for the meta-analysis. Electronic databases were scanned by 3 independent researchers. Funnel plot, Galbraith plot, and Egger Regression and Begg & Mazumdar Rank Correlation statistical analyzes were used to determining the publication bias. the odds ratio was computed through the Mantel-Haenszel WA with 95% confidence intervals. 12 studies were included in the meta-analysis. Results: in the No improvement vs Partial healing + Complete healing model, the lichenoid lesions in the positive patch WA group were associated with dental amalgam significantly (OR = 1.45, 95% CI: 1.01, 2.07) (p = 0.022). in the No improvement + Partial healing vs Complete healing model, the lichenoid lesions in the positive patch WA group were associated with dental amalgam significantly (OR = 1.92, 95% CI: 1.24, 2.96) (p = 0.001), too. Homogeneity was also identified according to the Cochrane Q and I-2 statistics in both of the analysis (P-Q = 0.826, I-2 = 00.00%). Conclusion: Patch testing is an effective method for detecting the relationship of oral lichenoid lesions with dental amalgams. Further studies should be done to support this hypothesis
Evaluation of Color Stability of Experimental Dental Composite Resins Prepared from Bis-EFMA, A Novel Monomer System
No full textColor stabilities of experimental composite resins based on Bis-EFMA (a novel bisphenol A [BPA]-free monomer system) with 3M ESPE FiltekTM Z250 (FZ) and experimental composite resins based on bisphenol A-glycidyl methacrylate (Bis-GMA) and urethane dimethacrylate (UDMA) were compared. Bis-EFMA was synthesized via the reaction between 9,9-bis[4-(2-hydroxyethoxy)phenyl]fluorene and 2-(methacryloyloxy)ethyl isocyanate. Experimental Bis-EFMA-, Bis-GMA-, and UMDA-based composites were prepared (20% of each of Bis-EFMA, Bis-GMA, UDMA, or triethylene glycol dimethacrylate (TEGDMA) and 60% glass filler). Eighty composite resin materials were produced (n=5). The initial color values of composites on the first day, first week, and after the first month after immersion into black tea, coffee, cola, and water solutions were measured using a spectrophotometer (VITA Easyshade (R) V; Zahnfabrik, Bad Sackingen, Germany) against a white background. UDMA- and Bis-EFMA-based composite resins exhibited significantly less Delta E and Delta L compared to Bis-GMA based composite resins (p 0.05). Tea and coffee caused significant changes in total color, light value, red-green, and blue-green coordinate values changes (Delta E, Delta L, Delta a, and Delta b, respectively) compared to water and cola (p < 0.05). At one month compared to one week and one day, Delta E, Delta L, Delta a, and Delta b were significantly different (p < 0.05). Bis-EFMA has the potential to be used in commercial dental composites as a substitute for Bis-GMA in terms of better color stability.Sutcu Imam University [2019/1-24 M]The authors would like to extend their appreciation to the Sutcu Imam University for funding the work (Project NO: 2019/1-24 M). Besides, they would like to thank Inci Dental Company who assists in the supply of experimental materials
Assessment of the Prevalence of Middle Mesial Canal in Mandibular First Molar: A Multinational Cross-sectional Study with Meta-analysis
No full textBackground: An additional canal found in the mandibular first molar (M1M) is the middle mesial canal (MMC), which is often missed during root canal treatment. In this study, the prevalence of MMC in M1M on cone-beam computed tomography (CBCT) images was evaluated in 15 countries, along with the effect of some demographic factors on its preva-lence. Methods: Deidentified CBCT images were scanned retrospectively, and the ones including bilateral M1Ms were included in the study. A written and video instruction program explaining the protocol to be followed step-by-step was provided to all observers to calibrate them. The CBCT imaging screening procedure consisted of evaluating three planes (coronal, sagittal, and axial) after a 3-dimensional alignment of the long axis of the root(s). The presence of an MMC in M1Ms (yes/no) was identified and recorded. Results: In total, 6304 CBCTs, representing 12,608 M1Ms, were evaluated. A significant difference was found between countries (P .05) or between genders (odds ratio= 1.07, 95% CI: 0.91, 1.27; P > .05). As for the age groups, no significant differences were found (P > .05). Conclusions: The prevalence of MMC varies by ethnicity, but it is generally estimated at 7% worldwide. Physicians must pay close attention to the presence of MMC in M1M, especially for opposite M1Ms, due to the prevalence of MMC being significantly bilateral. (J Endod 2023;49:549-558.
Automatic deep learning detection of overhanging restorations in bitewing radiographs
No full textObjectives This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.Methods A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.Results The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.Conclusions The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures
