OBJECTIVE:To compare the success and survival rates of metal-ceramic crowns and direct composite resin restorations retained with glass fiber posts, and to identify causes of failure during extended follow-up. MATERIAL AND METHODS:A prospective randomized controlled trial (NCT01461239) with parallel groups was conducted. Eighty-three teeth from 68 patients were initially randomized; however, six did not receive the assigned treatment. Thus, 77 teeth from 62 patients (42 composite resin, 35 metal-ceramic crown) with a glass fiber post and at least one remaining wall were analyzed. Standardized procedures were used, with annual follow-ups for up to 14 years. Failures were classified as repairable or non-repairable. Cox regression with shared frailty and Kaplan-Meier curves were used for statistical analysis. RESULTS:The median follow-up was 8.1 years for success and 10.58 years for survival. Overall, 10-year cumulative success and survival rates were 57.7% and 72.2%, respectively. Crowns had higher success (75.2%) and survival (79.4%) rates than composite resin (40.0% and 57.6%). Composite resin showed a significantly higher failure risk (HR = 3.53; p < 0.001), although survival did not differ statistically (p = 0.753). Most failures in composite resins were repairable (70%), while crown failures were mostly non-repairable (70%). At the final follow-up, 13 teeth were lost to follow-up in each group. CONCLUSIONS:Metal-ceramic crowns demonstrated superior long-term success and required fewer repairs than composite resin restorations. CLINICAL SIGNIFICANCE:For endodontically treated teeth, material selection influences outcomes. Metal-ceramic crowns provide longevity, while composite resin restorations offer the advantage of repairability.

This case report describes post-orthodontic direct composite restorations for anterior tooth wear using bilayered, three-dimensional (3D)-printed clear indices with a fully digital workflow. A patient with moderate incisal wear affecting the six maxillary anterior teeth underwent intraoral and facial scanning, followed by the creation of two digital wax-ups. Two sets of bilayered indices were fabricated, consisting of a rigid transparent outer shell for structural stability and a flexible inner liner for precise adaptation. Restoration was performed in two phases: alternate teeth were restored with the skipping-teeth first index, followed by restoration of the remaining teeth using the full-contour second index. The index design facilitated stable seating and controlled composite injection (Clearfil Majesty ES Flow Universal; Kuraray Noritake), which minimized material excess and simplified the finishing process while maintaining the planned morphology. The patient reported high satisfaction with comfort and treatment efficiency. At 15-month follow-up, all restorations remained intact with excellent marginal adaptation, surface quality, and esthetics. Within the limitations of this report, the use of bilayered 3D-printed clear indices suggests a feasible and conservative approach for managing anterior tooth wear.

PURPOSE:Minimal evidence exists on how class II cavity complexity influences direct restoration provision or if complexifying factors influence the use of techniques and materials. This study aimed to explore: (1) experts' willingness to restore a variety of specific presentations of class II cavities with direct composite restorations; (2) the influence of cavity characteristics on this and the provision of cuspal coverage; and (3) how varying cavity presentations affected technique and material use. METHODS AND MATERIALS:An e-questionnaire developed by international experts was distributed to 67 experts on direct composite restoration. The questionnaire section reported here presented vignettes of varying class II cavities, coded by potential complexifying factors. Respondents were asked if they would restore them with a direct composite. If so, it then asked what specific materials and techniques would be used. Regression analyses were performed. RESULTS:Vignette response rate was 78%. There was a large variation in willingness to restore varying class II cavity presentations with direct composite. This was significantly influenced by the presence of large inter-proximal 'bridging' gaps, root fillings, broad boxes, ≥ 3 cavity surfaces involved and deep sub-gingival margins. When present, these complexifying characteristics affected material and technique use. Those willing to restore more complex cavities were more likely to use more curved circumferential bands and less likely to use a rubber dam, among other factors. CONCLUSION:Cavity complexity affects experts' willingness to place direct composite restorations. The willingness to use a variety of techniques was associated with a greater readiness to restore more complex class II cavities with direct composite.

This study evaluated the methodological quality and presence of spin in systematic reviews with meta-analyses comparing single-visit and multiple-visit endodontic treatment, and investigated factors associated. Systematic reviews of randomised clinical trials were included without restrictions on language or publication year. Narrative reviews, reviews without meta-analysis, non-randomised or non-clinical studies, and conference abstracts were excluded. A comprehensive search was conducted in PubMed, Scopus, Web of Science, the Cochrane Library and grey literature in January 2025. Spin was classified using the Yavchitz et al. framework, and methodological quality was assessed with AMSTAR 2. Data extraction and spin assessment were performed independently and in duplicate. Thirteen systematic reviews were included. Spin was identified in 12 studies of abstracts and 10 of full texts, mainly as a misleading interpretation. Most reviews were rated as low or critically low quality. Positive results and absence of risk-of-bias assessment were associated with increased spin in full texts.

AIM:This prognostic study aims to develop a machine learning (ML) survival model for estimating the longevity (success and survival rate) of restorations in endodontically treated teeth (ETT). METHODOLOGY:Data were consolidated from four controlled clinical trials conducted in the Netherlands and Brazil, involving 424 patients and 618 restorations with up to 17 years of follow-up. The evaluated predictive models included Gradient Boosting Survival, Random Survival Forests and Survival Support Vector Machine. The dataset was split into 70% for training and 30% for testing. Hyperparameter tuning was optimised via 10-fold cross-validation with 50 iterations using hyperopt. Performance was assessed through the time-dependent area under the ROC curve (AUC), concordance index (C-index), inverse probability of censoring weights (IPCW C-index) and time-dependent Brier score. RESULTS:The Gradient Boosting Survival model achieved the highest AUC mean (0.83, 95% confidence interval [CI], 0.81-0.78), C-index (0.80), IPCW C-index (0.78) and Brier score (0.06) for survival rate predictions, maintaining predictive stability over time. For success rate, the Random Survival Forest model outperformed others (AUC = 0.73, 95% CI [0.70-0.75]), C-index (0.66), IPCW C-index (0.64) and Brier score (0.14). SHAP analysis identified patient age and tooth type as having the highest variable importance for survival, while the dentist's experience was critical for success outcomes. Fairness analysis revealed performance disparities across sexes and countries in the models. CONCLUSIONS:The models demonstrated high predictive performance, mainly in survival rate prediction. ML models show promise for developing a robust, data-driven framework to evaluate success and survival outcomes in ETT.

OBJECTIVES:This systematic review aimed to evaluate the failure of different types of restorative treatments for tooth wear. STUDY DESIGN:A search was conducted in Medline, Cochrane, Web of Science, SCOPUS, and Embase (October 2023) with no limits for publication year or language. Randomized and non-randomized studies comparing restorative options to treat moderate to severe tooth wear were included. Two reviewers independently selected studies, extracted data and assessed the risk of bias. Failure data was obtained from each study and organised into either 'major failure,' with the need to replace the restoration, or 'minor failure,' where the restoration was repaired or refurbished. Studies that did not bring comparisons or sufficient data to calculate failures were excluded. RESULTS:3977 articles were found; 43 studies were eligible for analysis. For RCT studies (n = 6), direct composite showed a mean annual failure rate (AFR) of 10.54 % for minor failures and 8.38 % for major failures. For non-RCT studies (n = 37), these were 3.97 % and 0.4 % respectively. For RCT studies, indirect composite showed a mean AFR of 12.84 % for minor failures and 10.41 % for major failures. For non-RCT studies, these were 2.9 % and 0.15 % respectively. For RCT studies, indirect ceramic showed a mean AFR of 0.09 % for minor failures and 0.13 % for major failures. For non-RCT studies, these were 0.83 % and 0.33 % respectively. CONCLUSION:Indirect restorations demonstrated lower failure rates; however, they can be more invasive and require more operator time than alternatives. Direct methods showed greater failures but offer a minimally invasive modality. (CRD42022358586) CLINICAL SIGNIFICANCE: This study will provide clinicians with a more informed view of the success, survival and failure rates of materials when deciding how to restore tooth wear.

OBJECTIVES:Considering the importance of distinguishing between primary and permanent teeth in children with mixed dentition, this study aimed to develop and evaluate an automated method for segmenting and labelling primary and permanent teeth in digital impressions. METHODS:716 digital impressions from 351 patients with primary or mixed dentitions were collected from the Netherlands, Brazil, and the 3DTeethSeg22 challenge dataset. The scans were annotated with tooth segmentations and primary and permanent teeth FDI numbers. A deep learning model was applied that combined large-context predictions for tooth labelling with high-resolution predictions for tooth segmentation. Using the collected scans, the model was trained and evaluated with five-fold cross-validation for tooth detection (F1-score), tooth segmentation (Dice score), and tooth labelling (macro-F1). Additionally, the model was trained and evaluated using the train-test split of the 3DTeethSeg22 challenge dataset. RESULTS:The developed model achieved highly effective results for tooth detection (F1-score = 0.996), tooth segmentation (Dice = 0.969), and tooth labelling (macro-F1 = 0.989). Moreover, a digital impression was processed in under two seconds on average. Furthermore, the proposed method outperformed the top-ranked 3DTeethSeg22 challenge submission (score = 0.954 vs. 0.976) and was particularly effective for tooth labelling (tooth identification rate = 0.910 vs. 0.955). Failure cases revealed mistakes for unusual dental conditions or ambiguous tooth eruption patterns. CONCLUSIONS:A highly effective algorithm for tooth segmentation was developed to differentiate between primary and permanent teeth in digital impressions. This fast and accurate model can benefit dentists in documenting children's teeth during the mixed dentition stage. CLINICAL SIGNIFICANCE:The algorithm provides an accurate and reliable tool for AI-assisted identification and numbering of primary and permanent teeth in digital impressions obtained from children with mixed dentition, thereby enhancing clinical workflow, improving treatment planning accuracy, and facilitating communication with patients and caregivers.

OBJECTIVES:Bitewings are commonly used radiographs for visualizing teeth and various dental conditions. Manual labeling and diagnosis on bitewings for chart filing are time-consuming and prone to observer-dependent variations. This multi-center study proposes a deep learning (DL) approach to automate comprehensive chart filing of bitewings. METHODS:A total of 1045 bitewings from Germany and The Netherlands were used for training and validation, and 216 from Slovakia for external testing. Annotations were performed by two dentists, one PhD researcher, and one caries expert. Hierarchical Mask DINO was developed for multi-class hierarchical end-to-end instance segmentation. Unmodified Mask DINO, SparseInst, and Mask R-CNN were used for comparison. Model performance was evaluated using F1-score, sensitivity, specificity, precision, mean average precision (mAP), and area under receiver operating characteristic curve (AUC). RESULTS:Mask DINO models exhibited high effectiveness for tooth segmentation and labeling, achieving precision, sensitivity, and F1-scores above 0.96. Hierarchical Mask DINO outperformed the other models in dental finding classification. F1-scores for implant, crown, pontic, filling, root canal treatment (RCT), caries lesion, and calculus deposit were 0.944, 0.918, 0.952, 0.956, 0.988, 0.749, and 0.758, respectively, with specificities all above 0.95. CONCLUSIONS:This study presented a DL-based approach for comprehensive assessment and diagnosis of bitewings, underlining its potential to enhance the efficiency and accuracy of chart filing in dental practice. CLINICAL SIGNIFICANCE:The proposed model provided fully automated tooth segmentation and numbering, along with comprehensive segmentation of dental conditions. Dental professionals can benefit from this model for reducing manual workload and enhancing clinical diagnosis.

Objective Literature was systematically reviewed to determine the impact of tooth wear management on the Oral Health Related Quality of Life (OHRQoL) amongst adult patients with tooth wear. Data A protocol was developed, a priori (PROSPERO CRD42022343108) following the PRISMA guidelines. To assess risk of bias and certainty of evidence the RoB2-tool, JBI-tool, and GRADE were used. Sources PubMed, Scopus, Cochrane Library, Embase and Web of Science were searched. The first search took place on 21.10.2022, subsequently updated in May 2024. Study selection Inclusion criteria were RCT's, quasi-RCT's, prospective- or retrospective-studies with adult patients with moderate to severe tooth wear, treated restoratively and/or with counseling and monitoring that were also assessed for OHRQoL during at least two time points. Exclusion criteria were, studies with children, OHRQoL only measured once, narrative and systematic reviews, conference abstracts, technical reports, consensus papers, and any other type of non-clinical study. Results Six papers were included in this review. Overall, qualitative analysis revealed an increase in OHRQoL after restorative treatment, and no change in OHRQoL after one year of counseling and monitoring. Some studies showed a slightly negative effect on esthetics in the years post-treatment, and some of the dimensions of the Oral Health Impact Profile (OHIP) did not change or demonstrated minor change only. For the RCT's, blinding of participants and operators was not possible, as the participants had an awareness of the treatment. For the non-RCT's, the primary issue was the lack of control, with a general high risk of bias. Conclusion The provision of restorative treatment in patients with moderate to severe tooth wear frequently results in a positive impact on OHRQoL. Further research is required to substantiate the importance of OHRQoL for the treatment of (tooth wear) patients. Clinical Significance The outcomes may help dentists and researchers better understand the advantages of using PROMS in their clinical work or research as valuable outcomes.

Purpose: This study aimed to reproduce and translate clinical presentations in an in vitro set-up and evaluate laboratory outcomes of mechanical properties (flexural strength, fatigue resistance, wear resistance) and link them to the clinical outcomes of the employed materials in the Radboud Tooth Wear Project (RTWP). Materials and methods: Four dental resin composites were selected. 30 discs (& Oslash;12.0 mm, 1.2 mm thick) were fabricated for each of Clearfil TM AP-X (AP), Filtek TM Supreme XTE (FS), Estenia TM C&B (ES), and Lava Ultimate (LU). Cyclic loading (200 N, 2 Hz frequency) was applied concentrically to 15 specimens per group with a spherical steatite indenter (r = 3.18 mm) in water in a contact-load-slide-liftoff motion (105 cycles). The wear scar was analysed using profilometry and the volume loss was digitally computed. Finally, all specimens were loaded (fatigued specimens with their worn surface loaded in tension) until fracture in a biaxial flexure apparatus. The differences in volume loss and flexural strength were determined using regression analysis. Results: Compared to AP and FS, ES and LU showed a significantly lower volume loss (p < 0.05). Non-fatigued ES specimens had a similar flexural strength compared to nonfatigued AP, while non-fatigued FS and LU specimens had a lower flexural strength (p < 0.001; 95 %CI: -80.0 - 51.8). The fatigue test resulted in a significant decrease of the flexural strength of ES specimens, only (p < 0.001; 95 %CI: -96.1 - -54.6). Clinical relevance: These outcomes concur with the outcomes of clinical studies on the longevity of these composites in patients with tooth wear. Therefore, the employed laboratory test seems to have the potential to test materials in a clinically relevant way.

Introduction: Despite the notable progress in developing artificial intelligence-based tools for caries detection in bitewings, limited research has addressed the detection and staging of secondary caries. Therefore, we aimed to develop a convolutional neural network (CNN)-based algorithm for these purposes using a novel approach for determining lesion severity. Methods: We used a dataset from a Dutch dental practice-based research network containing 2,612 restored teeth in 413 bitewings from 383 patients aged 15-88 years and trained the Mask R-CNN architecture with a Swin Transformer backbone. Two-stage training fine-tuned caries detection accuracy and severity assessment. Annotations of caries around restorations were made by two evaluators and checked by two other experts. Aggregated accuracy metrics (mean +/- standard deviation - SD) in detecting teeth with secondary caries were calculated considering two thresholds: detecting all lesions and dentine lesions. The correlation between the lesion severity scores obtained with the algorithm and the annotators' consensus was determined using the Pearson correlation coefficient and Bland-Altman plots. Results: Our refined algorithm showed high specificity in detecting all lesions (0.966 +/- 0.025) and dentine lesions (0.964 +/- 0.019). Sensitivity values were lower: 0.737 +/- 0.079 for all lesions and 0.808 +/- 0.083 for dentine lesions. The areas under ROC curves (SD) were 0.940 (0.025) for all lesions and 0.946 (0.023) for dentine lesions. The correlation coefficient for severity scores was 0.802. Conclusion: We developed an improved algorithm to support clinicians in detecting and staging secondary caries in bitewing, incorporating an innovative approach for annotation, considering the lesion severity as a continuous outcome.

Objective: This study presents a scoping review to determine the association between tooth wear and bruxism. Data: A protocol was developed a priori (Open Science Framework (DOI 10.17605/OSF.IO/CS7JX)). Established scoping review methods were used for screening, data extraction, and synthesis. Risk of bias was assessed using JBI tools. Direct associations between tooth wear and bruxism were assessed. Sources: Embase, SCOPUS, Web of Science, Cochrane, and PubMed were searched. Study selection: Any clinical study containing tooth wear and bruxism assessment done on humans in any language was included. Animal, in-vitro studies and case reports were excluded. Conclusions: Thirty publications reporting on the association between tooth wear and bruxism were included. The majority of publications were cross-sectional studies (90%) while only three were longitudinal (10%). Eleven papers assessed definitive bruxism for analysis (instrumental tools), one paper assessed probable bruxism (clinical inspection with self-report) and eighteen assessed possible bruxism (self-report). Of the eleven papers assessing definitive bruxism, eight also reported outcomes of non-instrumental tools. Tooth wear was mostly scored using indexes. Most studies reported no or weak associations between tooth wear and bruxism, except for the studies done on cervical tooth wear. When bruxism assessment was done through self-report, more often an association was found. Studies using multivariate analyses did not find an association between tooth wear and bruxism, except the cervical wear studies. Evidence shows inconclusive results as to whether bruxism and tooth wear are related or not. Therefore, well-designed longitudinal trials are needed to address this gap in the literature. Clinical significance: Based on the evidence, dental clinicians should not infer bruxism activity solely on the presence of tooth wear.

OBJECTIVES:This study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. METHODS:Eight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. RESULTS:The automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. CONCLUSIONS:The proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. CLINICAL SIGNIFICANCE:General practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Objective Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. Materials and methods Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. Results The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model’s performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. Conclusions The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.

OBJECTIVE:Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection. This study aimed to develop a convolutional neural network (CNN)-based algorithm for detecting primary caries and secondary caries around restorations using bitewings.METHODS:Clinical data from 7 general dental practices in the Netherlands, comprising 425 bitewings of 383 patients, were utilized. The study used the Mask-RCNN architecture, for instance, segmentation, supported by the Swin Transformer backbone. After data augmentation, model training was performed through a ten-fold cross-validation. The diagnostic accuracy of the algorithm was evaluated by calculating the area under the Free-Response Receiver Operating Characteristics curve, sensitivity, precision, and F1 scores.RESULTS:The model achieved areas under FROC curves of 0.806 and 0.804, and F1-scores of 0.689 and 0.719 for primary and secondary caries detection, respectively.CONCLUSION:An accurate CNN-based automated system was developed to detect primary and secondary caries lesions on bitewings, highlighting a significant advancement in automated caries diagnostics.CLINICAL SIGNIFICANCE:An accurate algorithm that integrates the detection of both primary and secondary caries will permit the development of automated systems to aid clinicians in their daily clinical practice.

OBJECTIVES:This study aimed to compare the success and survival rates of metal-ceramic crowns and composite resin restorations applied in root filled teeth that received a glass fiber post. METHODS:A prospective, randomized controlled trial, with equivalent parallel groups was designed. Eighty-two teeth were randomly allocated to the metal-ceramic or composite resin groups. Multivariate Cox regression analysis with shared frailty for patients and Kaplan-Meier curves were performed using success and survival rates (p<0.05). RESULTS:Seventy-five post-retained restorations (34 metal-ceramic crowns and 41 composite restorations) in 62 patients were analyzed. The median follow-up was 8.1 years [IQR 4.0-9.9]. Twenty-seven failures were observed. Twenty-two failures (81.5 %) were observed in the composite resin group, of which six (27.3 %) were not repairable. Five failures (18.5 %) were observed in the metal-ceramic crown group, of which three (66.6 %) were non-repairable. The cumulative success rate at 8 years was 85.0 % for crowns (AFR=1.31 %) and 43.2 % for composite resins (AFR=6.58 %), while the survival rate was 93.8 % for crowns (AFR=0.52 %) and 97.6 % for composite resins (AFR=0.20 %). Considering the success rates, adjusted multivariate Cox regression showed that composite resin had a Hazard Ratio of 5.07 (95 %CI, 1.99-12.89) greater than the metal-ceramic crown. No significant difference in the failure risk was observed when the survival rates were considered (HR=0.38, 95 %CI (0.10 - 1.44), p = 0.156). Co-variables did not affect the success and survival rates (p>0.05). CONCLUSIONS:Metal-ceramic crowns showed a higher success rate than composite restorations. The survival rates were similar, but composite restorations presented a higher need for repairs. CLINICAL SIGNIFICANCE:Post-retained composite restorations may need more reinterventions during the lifecycle, although more preservation of sound tooth structure is expected with a large restoration of resin post-and-core. These aspects have to be discussed with the patient for decision-making planning.

Dental materials are challenged by wear processes in the oral environment and should be evaluated in laboratory tests prior to clinical use. Many laboratory wear-testing devices are high-cost investments and not available for cross-centre comparisons. The 'Rub&Roll' wear machine enables controlled application of force, chemical and mechanical loading, but the initial design was not able to test against rigid antagonist materials. The current study aimed to probe the sensitivity of a new 'Rub&Roll' set-up by evaluating the effect of force and test solution parameters (deionized water; water + abrasive medium; acid + abrasive medium) on the wear behaviour of direct and indirect dental resin-based composites (RBCs) compared with human molars against 3D-printed rod antagonists. Molars exhibited greater height loss than RBCs in all test groups, with the largest differences recorded with acidic solutions. Direct RBCs showed significantly greater wear than indirect RBCs in the groups containing abrasive media. The acidic + abrasive medium did not result in increased wear of RBC materials. The developed method using the 'Rub&Roll' wear machine in the current investigation has provided a sensitive wear test method to allow initial screening of resin-based composite materials compared with extracted human molars under the influence of different mechanical and erosive challenges.

BACKGROUND:Tooth wear is the loss of dental hard tissue due to chemical and mechanical processes, and its prevalence ranges from 13 to 80 % in the general population. Management depends on understanding potential risk factors; however, the role of saliva as one of them is not completely understood. The aim of this study was to explore the relationship between salivary pH and flow, and tooth wear in patients referred to a specialized dental clinic for tooth wear management. METHODOLOGY:Data used in this study included stimulated (SWS) and unstimulated whole salivary (UWS) pH and flow rate. Dependent variables were the average occlusal Tooth Wear Index (TWI) and the average of the surfaces with the maximum Tooth Wear Evaluation System 2.0 score (TWES). Univariate and multivariate linear regression models were utilized, including a multivariate analysis without outliers. Sex and age were added as confounders. RESULTS:A total of 159 patients were included in this study. The average age of the individuals was 37.1 (± 9.1) years and 34 (21 %) were female. Univariate models showed a statistically significant association between both TWI and SWS pH. Multivariate models showed that the negative associations between SWS (β = -0.20, C.I. = -0.36 - -0.03 [TWI]; β = -0.12, C.I. = -0.22 - -0.02 [TWES]) and UWS pH (β = -0.12, C.I. = -0.26 - 0.02 [TWI]; β = -0.09, C.I. = -0.18 - 0.00 [TWES]) and tooth wear were largely unaffected by confounders. These associations were also robust against outliers. A relevant association with flow rate was not detected. CONCLUSION:This study shows that salivary pH was inversely associated with tooth wear severity even after correction for confounders, such as flow rate, age, and sex. This association was especially significant for SWS. Although no causal relationship can be established, the results suggest a role of salivary pH in tooth wear in patients with moderate to severe tooth wear. No association was found between tooth wear and flow rate.