Accuracy of different approaches for detecting proximal root caries lesions in vitro.

OBJECTIVES: The objective was to evaluate the diagnostic accuracy of radiographic evaluation (XR), visual-tactile assessment (VT), laser-fluorescence (LF) (DIAGNOdent Pen/KaVo), and near-infrared-light transillumination (NILT) (DIAGNOcam/KaVo) on proximal root caries lesions in vitro. METHODS: Two-hundred extracted permanent premolars and molars with and without proximal root caries lesions were allocated to 50 diagnostic models simulating the proximal contacts between teeth and mounted in a phantom dummy head. Two independent examiners used the diagnostic approaches to detect any or advanced root caries lesions, with histologic evaluation of the lesions serving as reference. Receiver operating characteristic (ROC) curves were employed, and sensitivity, specificity, and the area under the ROC curve (AUC) are calculated. Significant differences in mean AUCs between approaches were assumed if p < 0.05 (two-sample t-test). RESULTS: NILT was not applicable for proximal root caries detection. The sensitivity/specificity to detect any lesions was 0.81/0.63 for XR, 0.76/0.88 for VT and 0.81/0.95 for LF, and the sensitivity/specificity to detect advanced lesions was 0.43/0.94 for XR, 0.66/0.99 for VT, and 0.83/0.78 for LF, respectively. For both, any and advanced root caries lesions, mean AUCs for LF and VT were significantly higher compared to XR (p < 0.05). For any root caries lesions, LF was significantly more accurate than VT (p = 0.01), but not for advanced root caries lesions (p = 0.59). CONCLUSIONS: Under the in vitro conditions chosen, LF and VT were more accurate than XR to detect proximal root caries lesions, with LF being particularly useful for initial lesion stages. CLINICAL RELEVANCE: LF might be a useful diagnostic aid for proximal root caries diagnosis. Clinical studies are necessary to corroborate the findings.

Preventing and Arresting Primary Tooth Enamel Lesions Using Self- Assembling Peptide P11-4 In Vitro.

Objectives: To evaluate self-assembling peptides (SAP) for caries prevention and arrest in primary tooth enamel in vitro. Materials and Methods: Overall, 180 extracted primary teeth were used. In the prevention experiment (n = 20 samples per group), self-assembling peptide for prevention (SAPP), fluoride varnish/mouthwash (FV/FMW), casein-phosphopeptide amorphous-calcium phosphate (CPP-ACP), and nanohydroxyapatite (nHA) were applied. Samples were subjected to a demineralizing pH cycling for 14 days. In the arrest experiment (n = 15/group), 60 samples were pre-demineralized; induced lesions were treated using self-assembling peptide for repair (SAPR), FV, CPP-ACP plus fluoride, and resin infiltration (RI) and submitted to pH cycling. Mineral loss and its differences as well as lesion depth were determined using transversal microradiography. Numerical data were tested for normality using Shapiro-Wilk's test and were compared using Kruskal-Wallis test followed by pairwise comparisons utilizing multiple Mann-Whitney U tests with Bonferroni correction. The significance level was set at P < 0.05 within all tests. Results: FV (median: -46.3 [interquartile range: 175.52] vol% × µm) and FMW (-33.35 [124.65] vol% × µm) prevented caries significantly more effectively than all other groups (P < 0.001), which did not show significant preventive effects. RI (median: 4949.70 [1637.20] vol% × µm) and FV (median = 6076.05 [5190.08] vol% × µm) arrested lesions, whereas SAPR and CPP-ACPF did not show such arrest. Conclusions: FV and FMW showed the largest caries-preventive effect, whereas RI and FV arrested lesion progression in primary tooth enamel in vitro.

Dental Students' Knowledge, Attitudes and Beliefs Regarding Molar Incisor Hypomineralization (MIH): A Survey in Vienna, Austria.

PURPOSE: Molar incisor hypomineralization (MIH) is a frequently encountered dental condition worldwide. The aim of this study was to evaluate the knowledge of Austrian dental students about MIH. METHODS: A validated paper-based survey was distributed among 100 final year dental students at the Medical University of Vienna to assess their knowledge, perception and believes regarding MIH. One hundred dental students at the University Dental Clinic of Vienna were included in the study. At the time of the survey, they were in their 11th or 12th semester (final year). Information about the knowledge on diagnosis and prevalence, and attitudes and beliefs around etiology and management was collected. RESULTS: All students replied, 94 questionnaires could be included in the analysis (50% female). 98% of the students were familiar with MIH and 86% were aware with the clinical features of MIH. Only 13% reported clinical ability to identify MIH. Regarding the etiology of MIH, most of the students (69%) named "genetic factors" as the most frequent etiological factor. CONCLUSION: Dental students showed great interest in the topic of MIH, as the majority of all respondents were in favor of deepening their knowledge regarding this topic. The students should be offered opportunities to do this both during and after their studies.

Knowledge, attitudes, and beliefs regarding molar incisor hypomineralisation amongst Swiss dental students.

BACKGROUND: Knowledge obtained at the undergraduate level regarding molar incisor hypomineralisation (MIH) has an impact on future practice of dentists and paediatric dentists. This cross-sectional study aimed to assess final-year dental students' knowledge, attitudes and beliefs towards MIH in all Swiss universities. METHODS: A previously utilised survey (in both English and German) was distributed among final-year dental students in all Swiss dental schools (Basel, Bern, Geneva and Zurich). It probed students' knowledge, attitudes and beliefs regarding the diagnosis, prevalence, aetiology, and management of MIH, and was structured in two parts: knowledge/perception and clinical application. The students' responses were analysed statistically with descriptive statistics. RESULTS: 113 out of 133 final-year Swiss dental students took part in the study (85%). Nearly all students were familiar with MIH (99%), but only 12% of them felt confident when diagnosing MIH clinically. Direct composite fillings (66%), indirect restorations (28%) and preformed stainless-steel crowns (26%) were chosen as most suitable treatment options for MIH-affected teeth. CONCLUSION: Final-year Swiss dental students are well informed about MIH. However, they report low level of confidence when clinically confronted with MIH-affected teeth regarding its diagnosis and treatment. Swiss Universities curricula should be revisited accordingly.

Deep learning for cephalometric landmark detection: systematic review and meta-analysis.

OBJECTIVES: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. METHODS: Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). DATA: From 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). CONCLUSIONS: DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. CLINICAL SIGNIFICANCE: Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

Generalizability of Deep Learning Models for Caries Detection in Near-Infrared Light Transillumination Images.

OBJECTIVES: The present study aimed to train deep convolutional neural networks (CNNs) to detect caries lesions on Near-Infrared Light Transillumination (NILT) imagery obtained either in vitro or in vivo and to assess the models' generalizability. METHODS: In vitro, 226 extracted posterior permanent human teeth were mounted in a diagnostic model in a dummy head. Then, NILT images were generated (DIAGNOcam, KaVo, Biberach), and images were segmented tooth-wise. In vivo, 1319 teeth from 56 patients were obtained and segmented similarly. Proximal caries lesions were annotated pixel-wise by three experienced dentists, reviewed by a fourth dentist, and then transformed into binary labels. We trained ResNet classification models on both in vivo and in vitro datasets and used 10-fold cross-validation for estimating the performance and generalizability of the models. We used GradCAM to increase explainability. RESULTS: The tooth-level prevalence of caries lesions was 41% in vitro and 49% in vivo, respectively. Models trained and tested on in vivo data performed significantly better (mean ± SD accuracy: 0.78 ± 0.04) than those trained and tested on in vitro data (accuracy: 0.64 ± 0.15; p < 0.05). When tested in vitro, the models trained in vivo showed significantly lower accuracy (0.70 ± 0.01; p < 0.01). Similarly, when tested in vivo, models trained in vitro showed significantly lower accuracy (0.61 ± 0.04; p < 0.05). In both cases, this was due to decreases in sensitivity (by -27% for models trained in vivo and -10% for models trained in vitro). CONCLUSIONS: Using in vitro setups for generating NILT imagery and training CNNs comes with low accuracy and generalizability. CLINICAL SIGNIFICANCE: Studies employing in vitro imagery for developing deep learning models should be critically appraised for their generalizability. Applicable deep learning models for assessing NILT imagery should be trained on in vivo data.

Detecting white spot lesions on dental photography using deep learning: A pilot study.

OBJECTIVES: We aimed to apply deep learning to detect white spot lesions in dental photographs. METHODS: Using 434 photographic images of 51 patients, a dataset of 2781 cropped tooth segments was generated. Pixelwise annotations of sound enamel as well as fluorotic, carious or other types of hypomineralized lesions were generated by experts and assessed by an independent second reviewer. The union of the reviewed annotations were used to segment the hard tissues (region-of-interest, ROI) of each image. SqueezeNet was employed for modelling. We trained models to detect (1) any white spot lesions, (2) fluorotic lesions and (3) other-than-fluorotic lesions. Modeling was performed on both the cropped and the ROI images and using ten-times repeated five-fold cross-validation. Feature visualization was applied to visualize salient areas. RESULTS: Lesion prevalence was 37 %; the majority of lesions (24 %) were fluorotic. None of the metrics differed significantly between the models trained on cropped and ROI imagery (p > 0.05/t-test). Mean accuracies ranged between 0.81-0.84, without significant differences between models trained to detect any, fluorotic or other-than-fluorotic lesions (p > 0.05). Specificities were 0.85-0.86; sensitivities were lower (0.58-0.66). Models to detect any lesions showed positive/negative predictive values (PPV/NPV) between 0.77-0.80, those to detect fluorotic lesions 0.67 (PPV) to 0.86 (NPV), and those to detect other-than-fluorotic lesions 0.46 (PPV) to 0.93 (NPV). Light reflections were the main reason for false positive detections. CONCLUSIONS: Deep learning showed satisfying accuracy to detect white spot lesions, particularly fluorosis. Some models showed limited stability given the small sample available. CLINICAL SIGNIFICANCE: Deep learning is suitable for automated classification of retro- or prospectively collected imagery and may assist practitioners in discriminating white spot lesions. Future studies should expand the scope into more granular multi-class detections on a larger and more generalizable dataset.

Selective vs stepwise removal of deep carious lesions in primary molars: 24 months follow-up from a randomized controlled trial.

OBJECTIVES: For well-defined deep (> 2/3 dentin extension) carious lesions, selective (SE) or stepwise (SW) carious tissue removals have been recommended, while there is limited comparative evidence for both. We compared SE and SW over 24 months in a randomized controlled trial. METHODS: A two-arm superiority trial was conducted comparing SW/SE in primary molars without pulpal symptoms but well-defined deep lesions. Seventy-four children (1 molar/child) aged 3-9 years were recruited. In a first step, peripheral carious tissue was removed until hard dentin remained, while in proximity to the pulp, leathery dentin was left. An adhesive compomer restoration was placed and restorations re-examined after 6 months. In SW, re-entry and removal to firm dentin was conducted pulpo-proximally, followed by re-restoration. Molars were re-evaluated for a total of 24 months. Our primary outcome was success (absence of restorative/endodontic complications or pulp exposures). Secondary outcomes included total treatment and opportunity costs and restoration quality, assessed using modified USPHS criteria. RESULTS: After 24 months, 63 molars (31 SE, 32 SW) were re-assessed. Four failures occurred (2 exposures in SW; 2 pulpal complications in SE, 1 of them leading to extraction, p > 0.05). Restoration integrity was satisfying in both groups (USPHS A/B/C in 21/8/0 SE and 23/7/0 SW, p > 0.05). Treatment and opportunity costs were significantly higher in SW than SE (mean 171 ± 51 vs. 106 ± 90; p < 0.001). CONCLUSIONS: After 2 years, SE and SW showed similar efficacy for managing deep carious lesions in primary molars. The higher costs for SW should be considered during decision-making. CLINICAL SIGNIFICANCE: In primary molars with well-defined deep carious lesions SE was less costly and similarly efficacious like SW. From a cost and applicability perspective, SW may need to be indicated restrictively, e.g., for very deep (> 3/4 dentin extension) lesions only. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02232828.

Knowledge, attitudes, and beliefs regarding molar incisor hypomineralization (MIH) amongst German dental students.

BACKGROUND: Knowledge of molar incisor hypomineralization (MIH) has relevance for paediatric dentists. AIM: To assess final-year German dental students' knowledge, attitudes, and beliefs regarding MIH. MATERIALS AND METHODS: A previously validated questionnaire was posted to the 31 German dental schools. Demographic covariates as well as knowledge regarding diagnosis and prevalence, and attitudes and beliefs around aetiology and management were collected. RESULTS: Twenty-two (71%) dental schools responded and a total of 877 students participated. Most (97%) were familiar with MIH and 88% were aware of the diagnostic criteria for MIH; however, only 42% knew how to implement them. One-third were able to identify MIH and 16% reported diagnostic confidence when doing so; 90% assumed the MIH prevalence to be <10%. Two-thirds of the respondents implicated genetic components as the main aetiological factor of MIH. Resin composite (60%) and preformed metal crowns (46%) were the dental materials most often suggested for restorative management. Almost all (98%) respondents were interested in receiving more clinical training. CONCLUSION: German students were familiar with MIH; however, they reported low levels of knowledge and confidence regarding its prevalence and diagnosis. Standardized nationwide, up-to-date curricula should be implemented to educate future dentists in Germany.

Detecting caries lesions of different radiographic extension on bitewings using deep learning.

OBJECTIVES: We aimed to apply deep learning to detect caries lesions of different radiographic extension on bitewings, hypothesizing it to be significantly more accurate than individual dentists. METHODS: 3686 bitewing radiographs were assessed by four experienced dentists. Caries lesions were marked in a pixelwise fashion. The union of all pixels was defined as reference test. The data was divided into a training (3293), validation (252) and test dataset (141). We applied a convolutional neural network (U-Net) and used the Intersection-over-Union as validation metric. The performance of the trained neural network on the test dataset was compared against that of seven independent using tooth-level accuracy metrics. Stratification according to lesion depth (enamel lesions E1/2, dentin lesions into middle or inner third D2/3) was applied. RESULTS: The neural network showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (min-max: 0.61-0.78, p < 0.05). The neural network was significantly more sensitive than dentists (0.75 versus 0.36 (0.19-0.65; p = 0.006), while its specificity was not significantly lower (0.83) than those of the dentists (0.91 (0.69-0.98; p > 0.05); p > 0.05). The neural network showed robust sensitivities at or above 0.70 for both initial and advanced lesions. Dentists largely showed low sensitivities for initial lesions (all except one dentist showed sensitivities below 0.25), while those for advanced ones were between 0.40 and 0.75. CONCLUSIONS: To detect caries lesions on bitewing radiographs, a deep neural network was significantly more accurate than dentists. CLINICAL SIGNIFICANCE: Deep learning may assist dentists to detect especially initial caries lesions on bitewings. The impact of using such models on decision-making should be explored.

The forgotten merits of GIC restorations: a systematic review.

OBJECTIVE: To reevaluate proven strengths and weakness of glass ionomer cements (GICs) and to identify agreement versus conflicting evidence in previous reports regarding the transition between GIC and the tooth, and the existence of an "interphase". MATERIALS AND METHODS: Relevant electronic databases (PubMed, Embase via Ovid and Medline via Web of science) were searched for publications of evidence relating to the transition zone at the GIC-tooth interphase. Studies were examined and grouped according to characteristics of GIC-tooth attachment area quantified by X-ray and optical microscopy techniques in 2D and 3D. RESULTS: Inclusion criteria comprised of in vitro studies that showed images of the conventional GIC-tooth substrate attachments using at least one of the following techniques: SEM, CLSM, or µCT. The search identified 419 studies, from which 33 were included. Ten studies demonstrated the existence of an interphase layer and five studies quantified the layer thickness (1-15 µ). Twenty-nine publications studied different failure modes of the GIC-tooth interphase. Eleven studies described discontinuities inside the GIC bulk. CONCLUSION: The GIC-tooth interphase attributes evolve with time. Good attachment is evident even under compromised surface preparation. The GIC-tooth attachment area is resistant to acidic dissolution as compared to both tooth and GIC bulk. In general, studies revealed mostly intact GIC-tooth interphases with only some cracked interphases. CLINICAL SIGNIFICANCE: GIC bonds to the tooth structure and forms an acid resistant attachment zone that might enhance caries inhibition. Due to fluoride release and ease of use, GIC provides a cost effective treatment, ideal for low income or high caries populations.

Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study.

OBJECTIVES: In this pilot study, we applied deep convolutional neural networks (CNNs) to detect caries lesions in Near-Infrared-Light Transillumination (NILT) images. METHODS: 226 extracted posterior permanent human teeth (113 premolars, 113 molars) were allocated to groups of 2 + 2 teeth, and mounted in a pilot-tested diagnostic model in a dummy head. NILT images of single-tooth-segments were generated using DIAGNOcam (KaVo, Biberach). For each segment (on average 435 × 407 × 3 pixels), occlusal and/or proximal caries lesions were annotated by two experienced dentists using an in-house developed digital annotation tool. The pixel-based annotations were translated into binary class levels. We trained two state-of-the-art CNNs (Resnet18, Resnext50) and validated them via 10-fold cross validation. During the training process, we applied data augmentation (random resizing, rotations and flipping) and one-cycle-learning rate policy, setting the minimum and maximum learning rates to 10-5 and 10-3, respectively. Metrics for model performance were the area-under-the-receiver-operating-characteristics-curve (AUC), sensitivity, specificity, and positive/negative predictive values (PPV/NPV). Feature visualization was additionally applied to assess if the CNNs built on features dentists would also use. RESULTS: The tooth-level prevalence of caries lesions was 41%. The two models performed similar on predicting caries on tooth segments of NILT images. The marginal better model with respect to AUC was Resnext50, where we retrained the last 9 network layers, using the Adam optimizer, a learning rate of 0.5 × 10-4, and a batch size of 10. The mean (95% CI) AUC was 0.74 (0.66-0.82). Sensitivity and specificity were 0.59 (0.47-0.70) and 0.76 (0.68-0.84) respectively. The resulting PPV was 0.63 (0.51-0.74), the NPV 0.73 (0.65-0.80). Visual inspection of model predictions found the model to be sensitive to areas affected by caries lesions. CONCLUSIONS: A moderately deep CNN trained on a limited amount of NILT image data showed satisfying discriminatory ability to detect caries lesions. CLINICAL SIGNIFICANCE: CNNs may be useful to assist NILT-based caries detection. This could be especially relevant in non-conventional dental settings, like schools, care homes or rural outpost centers.

Outcome and comparator choice in molar incisor hypomineralisation (MIH) intervention studies: a systematic review and social network analysis.

OBJECTIVES: Outcome and comparator choice strongly determine the validity and implementation of clinical trial results. We aimed to assess outcome and comparator choice in intervention studies on molar incisor hypomineralisation (MIH) using systematic review and social network analysis (SNA). DESIGN AND DATA SOURCES: Medline, Embase, Cochrane Central, Google Scholar, opengrey.eu as well as DRKS.de and Clinicaltrials.gov were searched for MIH intervention studies. The search covered the period from 1980 to 2019. ELIGIBILITY CRITERIA: Clinical single-arm/multiarm, controlled/uncontrolled studies reporting on the management of MIH were included. Reported outcomes and comparators were extracted and categorised. SNA was used to evaluate comparator choice and the resulting trial networks. DATA EXTRACTION: Of the 7979 identified records, 100 were evaluated in full text and 35 studies (17 randomised controlled trials, 14 prospective and 4 retrospective cohort studies) were included. RESULTS: In total, 2124 patients with a mean age of 11 years (min/max 6/70 years) were included. Outcomes fell in one of 11 different outcome categories: restoration success, aesthetic improvement, pain/hypersensitivity/discomfort, mineral gain, space management, anaesthesia effectiveness, preventive success, efficiency, quality of life, gingival and periodontal health and patient satisfaction. Comparators were mainly restorative interventions (17 studies), remineralisation (3), treatment of hypersensitivity (10), aesthetic interventions (5) and orthodontic interventions (3). Two highly clustered comparator networks emerged; many interventions were not robustly linked to these networks. CONCLUSIONS: MIH intervention studies recorded both clinically centred and patient-centred outcomes. Core outcome set development should consider these and supplement them with outcomes on, for example, applicability. The high number of compared interventions tested in only few studies and our SNA results implicate that the current evidence may not be robust.

Deep Learning for the Radiographic Detection of Apical Lesions.

INTRODUCTION: We applied deep convolutional neural networks (CNNs) to detect apical lesions (ALs) on panoramic dental radiographs. METHODS: Based on a synthesized data set of 2001 tooth segments from panoramic radiographs, a custom-made 7-layer deep neural network, parameterized by a total number of 4,299,651 weights, was trained and validated via 10 times repeated group shuffling. Hyperparameters were tuned using a grid search. Our reference test was the majority vote of 6 independent examiners who detected ALs on an ordinal scale (0, no AL; 1, widened periodontal ligament, uncertain AL; 2, clearly detectable lesion, certain AL). Metrics were the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive/negative predictive values. Subgroup analysis for tooth types was performed, and different margins of agreement of the reference test were applied (base case: 2; sensitivity analysis: 6). RESULTS: The mean (standard deviation) tooth level prevalence of both uncertain and certain ALs was 0.16 (0.03) in the base case. The AUC of the CNN was 0.85 (0.04). Sensitivity and specificity were 0.65 (0.12) and 0.87 (0.04,) respectively. The resulting positive predictive value was 0.49 (0.10), and the negative predictive value was 0.93 (0.03). In molars, sensitivity was significantly higher than in other tooth types, whereas specificity was lower. When only certain ALs were assessed, the AUC was 0.89 (0.04). Increasing the margin of agreement to 6 significantly increased the AUC to 0.95 (0.02), mainly because the sensitivity increased to 0.74 (0.19). CONCLUSIONS: A moderately deep CNN trained on a limited amount of image data showed satisfying discriminatory ability to detect ALs on panoramic radiographs.

Effects of calcium silicate cements on dental pulp cells: A systematic review.

OBJECTIVES: The aim of this study was to evaluate the biocompatibility, odontogenic, angiogenic and inflammatory effects of commercially available calcium silicate cements (CSCs) on dental pulp cells. DATA: In vitro, animal and human in vivo studies reporting on biocompatibility, odontogenic, angiogenic and inflammatory effects of CSCs on dental pulp cells were screened using a systematic review, and a descriptive analysis performed. SOURCES: We searched Medline via PubMed, Google Scholar and Scopus, followed by hand search and cross-referencing. STUDY SELECTION: From 7007 identified studies; 38 were included. At least one MTA-type product was evaluated in each study, with ProRooT MTA being the most frequently assessed, followed by Biodentine and iRoot BP Plus. Nearly all CSCs exhibited a high biocompatibility and induced odontogenic and angiogenic effects. There was great heterogeneity in methodology and findings. In vivo, effects differed between materials; also, differences between human or animal pulp cell effects were noted. In vitro, the dilution of the cement, the period of exposure to the CSC and the specific effect measure influenced the outcomes. No CSC was clearly superior to alternatives. CONCLUSIONS: All commercially available CSCs are biocompatible, exhibit comparable and favorable effects on odontogenic differentiation of dental pulp cells in vitro and can efficiently enhance dentin bridge formation of high quality with minimal inflammation. No specific CSC can be recommended. CLINICAL SIGNIFICANCE: Most CSCs are highly biocompatible, promoting pulp healing at minimal pulp inflammation. While the variation in methodology limits comparisons across studies, it seems that nearly all CSCs show favorable effects on dental pulp cell. We are unable to recommend one specific material over the others.

Selective vs stepwise removal of deep carious lesions in primary molars: 12-Months results of a randomized controlled pilot trial.

OBJECTIVES: For deep carious lesions, selective or stepwise carious tissue removal (SE, SW) seem advantageous compared with non-selective removal. For primary molars, there is insufficient evidence comparing SE against SW. This randomized pilot trial compared SE and SW over 12 months. METHODS: A two-arm superiority trial was conducted comparing SE and SW in primary molars with deep lesions but without pulpal symptoms. We recruited 74 children (one molar/child) aged 3-9 years. In both groups, peripheral carious tissue removal was performed at T1 to hard dentin. In proximity to the pulp, leathery dentin was left followed by an adhesive compomer restoration. Blinded re-examination was performed after six months (T2). Molars allocated to SW were re-entered, removal to firm dentin carried out pulpo-proximally, and again restored. After another 6 months, all molars were re-examined (T3). Our primary outcome was success, defined as no restorative/endodontic complications (including pulp exposure) leading to reinterventions. Secondary outcomes included total treatment and opportunity costs. Patients', dentists' and parents' subjective assessments were recorded. This trial was registered (ClinicalTrials.gov/NCT02232828). RESULTS: After 12 months a total of 72 children (36 SE, 36 SW) were analyzed. Three failures occurred (2 exposures in SW, 1 pulpal complication leading to extraction in SE) (p > 0.05). The subjective evaluation by patients, parents or dentists did not differ significantly. Combined treatment and opportunity costs were significantly higher in SW (mean;SD: 186;61 Euro) than SE (100;59) (p < 0.001). CONCLUSIONS: The significantly increased costs for performing SW instead of SE in deep carious lesions in primary molars may not be justified. CLINICAL SIGNIFICANCE: For primary molars with deep lesions, but vital pulps, SE was less costly at similar efficacy compared with SW. Dentists' decision-making should consider this alongside further clinical aspects.

Visual and radiographic caries detection: a tailored meta-analysis for two different settings, Egypt and Germany.

BACKGROUND: Diagnostic meta-analyses on caries detection methods should assist practitioners in their daily practice. However, conventional meta-analysis estimates may be inapplicable due to differences in test conduct, applied thresholds and assessed population between settings. Our aim was to demonstrate the impact of tailored meta-analysis of visual and radiographic caries detection to different settings using setting-specific routine data. METHODS: Published systematic reviews and meta-analyses on the accuracy of visual and radiographic caries detection were used. In two settings (a private practice in Germany and a public health clinic in Egypt), routine data of a total of 100 (n = 50/practice) consecutive 12-14 year-olds were collected. Test-positive rates of visual and radiographic detection for initial and advanced carious lesions on occlusal or proximal surfaces of molars were used to tailor meta-analyses. If prevalence data were available, these were also used for tailoring. RESULTS: From the original reviews, 210 and 100 heterogeneous studies on visual and radiographic caries detection were included in our meta-analyses. For radiographic detection, sensitivity and specificity estimates derived from conventional and tailored meta-analysis were similar. For visual detection of advanced occlusal carious lesions, the conventional meta-analysis yielded a sensitivity and specificity (95% CI) of 64.6% (57-71) and 90.9% (88-93), whereas the tailored estimates for Egypt were 75.1% (70-81) and 84.9% (82-89), respectively, and 43.7% (37-51) and 96.5% (95-97) for Germany, respectively. CONCLUSION: Conventional test accuracy meta-analyses may yield aggregate estimates which are inapplicable to specific settings. Routine data may be used to produce a meta-analysis estimate which is tailored to the setting and thereby improving its applicability.

In vitro performance of the DIAGNOcam for detecting proximal carious lesions adjacent to composite restorations.

OBJECTIVES: To assess the accuracy of near-infrared-light transillumination (DIAGNO) compared to visual-tactile (VT) and radiographic (RA) evaluation of proximal carious lesions adjacent to composite restorations in vitro. METHODS: Two hundred extracted posterior permanent human teeth with occluso-proximal composite restorations were allocated to 50 groups of four posterior teeth, and mounted in a pilot-tested diagnostic model in a dummy head. The teeth were independently assessed by two examiners. Transverse microradiography and visual assessment served as reference tests to detect any lesions (prevalence 24%) and cavitated lesions (18%), respectively, adjacent to restorations. Sensitivity, specificity, positive and negative predictive values and the area under the receiver-operating-characteristics curve (AUC) were calculated. RESULTS: To detect any proximal carious lesions adjacent to composite, the mean sensitivity/specificity were 0.63/0.95 for DIAGNO, 0.70/0.88 for RA when lesions radiographically extending into enamel and dentin were considered, 0.26/0.98 for RA when only lesions extending into dentin were considered, and 0.31/0.96 for VT. For cavitated lesions adjacent to proximal composite restorations, these values were RA (enamel and dentin) 0.84/0.88, RA (dentin) 0.34/0.99, DIAGNO 0.69/0.94 and VT 0.40/0.97. AUC did not differ significantly between RA and DIAGNO, while VT showed significantly lower values (p < 0.05). CONCLUSION: Within the limitations of this study, DIAGNO seems useful for detecting proximal carious lesions adjacent to restorations. CLINICAL RELEVANCE: Near-infrared-light transillumination could be used as a radiation-free adjunct or alternative to RA for detecting carious lesions adjacent to composite restorations.

Global burden of molar incisor hypomineralization.

OBJECTIVES: We aimed to systematically review and meta-analyze the global, super-regional, regional and national prevalence of molar-incisor-hypomineralization (MIH) and to determine the numbers of prevalent and incident cases on different spatial scales. The review was registered (PROSPERO CRD42017063842). SOURCES: Five electronic databases (Medline, EMBASE, LILACS, Web of Science, Google Scholar) were searched systematically. STUDY SELECTION: Observational studies on the prevalence of MIH were included and the prevalence on different spatial scales (global, super-regional, regional, national) synthesized using random-effects meta-analyses. The prevalence was then regressed on a large set of methodological, socioeconomic and environmental variables to estimate the global burden (incident and prevalent cases) of MIH. DATA: Of 2239 identified studies, 99 studies on 113,144 participants from 43 countries were included. The meta-analysis yielded a mean (95% CI) prevalence of 13.1% (11.8-14.5%), with significant differences between super-regions, regions and countries. The number of prevalent cases in 2015 was estimated at 878 (791-971) million people, while the number of incident cases in 2016 was 17.5 (15.8-19.4) million. Of these, 27.4% (23.5-31.7%) (in mean, 240 million prevalent and 4.8 million incident cases, respectively) were or will be in need of therapy due to pain, hypersensitivity or posteruptive breakdown. Heavily populated countries contribute significantly to the burden of prevalent cases, while growing countries like India, but also Pakistan or Indonesia rank first with respect to the number of incident cases. CONCLUSIONS: MIH is highly prevalent across the globe. Certain (mainly low- and middle income) countries shoulder the majority of this burden. Clinical significance The consistently high prevalence and the large proportion of cases in need of care should be considered by both clinicians in their daily practice and healthcare planners and policy makers.