Predicting mortality in the very old: a machine learning analysis on claims data.

Machine learning (ML) may be used to predict mortality. We used claims data from one large German insurer to develop and test differently complex ML prediction models, comparing them for their (balanced) accuracy, but also the importance of different predictors, the relevance of the follow-up period before death (i.e. the amount of accumulated data) and the time distance of the data used for prediction and death. A sample of 373,077 insured very old, aged 75 years or above, living in the Northeast of Germany in 2012 was drawn and followed over 6 years. Our outcome was whether an individual died in one of the years of interest (2013-2017) or not; the primary metric was (balanced) accuracy in a hold-out test dataset. From the 86,326 potential variables, we used the 30 most important ones for modeling. We trained a total of 45 model combinations: (1) Three different ML models were used; logistic regression (LR), random forest (RF), extreme gradient boosting (XGB); (2) Different periods of follow-up were employed for training; 1-5 years; (3) Different time distances between data used for prediction and the time of the event (death/survival) were set; 0-4 years. The mortality rate was 9.15% in mean per year. The models showed (balanced) accuracy between 65 and 93%. A longer follow-up period showed limited to no advantage, but models with short time distance from the event were more accurate than models trained on more distant data. RF and XGB were more accurate than LR. For RF and XGB sensitivity and specificity were similar, while for LR sensitivity was significantly lower than specificity. For all three models, the positive-predictive-value was below 62% (and even dropped to below 20% for longer time distances from death), while the negative-predictive-value significantly exceeded 90% for all analyses. The utilization of and costs for emergency transport as well as emergency and any hospital visits as well as the utilization of conventional outpatient care and laboratory services were consistently found most relevant for predicting mortality. All models showed useful accuracies, and more complex models showed advantages. The variables employed for prediction were consistent across models and with medical reasoning. Identifying individuals at risk could assist tailored decision-making and interventions

Posterior ceramic versus metal restorations: A systematic review and meta-analysis.

OBJECTIVES The goal of this systemic review and meta-analysis was to evaluate the longevity of indirect adhesively-luted ceramic compared to conventionally cemented metal single tooth restorations. DATA Randomized controlled trials (RCT) investigating indirect adhesively-luted ceramic restorations compared to metal or metal-based cemented restorations in permanent posterior teeth. SOURCES Three electronic databases (PubMed, CENTRAL (Cochrane) and Embase) were screened. No language or time restrictions were applied. Study selection, data extraction and quality assessment were done in duplicate. Risk of Bias and level of evidence was graded using Risk of Bias 2.0 tool and Grade Profiler 3.6. RESULTS A total of 3056 articles were found by electronic databases. Finally, four RCTs were selected. Overall, 443 restorations of which 212 were adhesively-luted ceramic restorations and 231 conventionally cemented metal restorations have been placed in 314 patients (age: 22-72 years). The highest annual failure rates were found for ceramic restorations ranging from 2.1% to 5.6%. Lower annual failure rates were found for metal (gold) restorations ranging from 0% to 2.1%. Meta-analysis could be performed for adhesively-luted ceramic vs. conventionally cemented metal restorations. Conventionally cemented metal restoration showed a significantly lower failure rate than adhesively-luted ceramic ones (visual-tactile assessment: Risk Ratio (RR)[95%CI]=0.31[0.16,0.57], low level of evidence). Furthermore, all studies showed a high risk of bias. CONCLUSION Conventionally cemented metal restorations revealed significantly lower failure rates compared to adhesively-luted ceramic ones, although the selected sample was small and with medium follow-up periods with high risks of bias

Costs for Statutorily Insured Dental Services in Older Germans 2012–2017

Objectives: We assessed the costs of dental services in statutorily insured, very old (geriatric) Germans. Methods: A comprehensive sample of very old (≥75 years) people insured at a large Northeastern statutory insurer was followed over 6 years (2012–2017). We assessed dental services costs for: (1) examination, assessments and advice, (2) operative, (3) surgical, (4) prosthetic, (5) periodontal, (6) preventive and (7) outreach services. Association of utilization with: (1) sex, (2) age, (3) region, (4) social hardship status, (5) International Disease Classification (ICD-10) diagnoses and (6) Diagnoses Related Groups (DRGs) was explored. Results: 404,610 individuals with a mean (standard deviation, SD) age 81.9 (5.4 years) were followed, 173,733 did not survive follow-up. Total mean costs were 129.61 (310.97) euro per capita; the highest costs were for prosthetic (54.40, SD 242.89 euro) and operative services (28.40, SD 68.38 euro), examination/advice (21.15, SD 28.77 euro), prevention (13.31, SD 49.79 euro), surgery (5.91, SD 23.91 euro), outreach (4.81, SD 28.56 euro) and periodontal services (1.64, SD 7.39 euro). The introduction of new fee items for outreach and preventive services between 2012 and 2017 was reflected in costs. Total costs decreased with increasing age, and this was also found for all service blocks except outreach and preventive services. Costs were higher in those with social hardship status, and in Berlin than Brandenburg and Mecklenburg-Western Pomerania. Certain general health conditions were associated with increased or decreased costs. Conclusions: Costs were associated with sex, social hardship status, place of living and general health conditions. Clinical significance: Dental services costs for the elderly in Germany are unequally distributed and, up to a certain age or health status, generated by invasive interventions mainly. Policy makers should incentivize preventive services earlier on and aim to distribute expenses more equally

Effects of Dentifrices Differing in Fluoride Content on Remineralization Characteristics of Dentin in vitro

Objectives: The aim of this study was to compare the caries preventive effect of highly fluoridated dentifrices and gels on sound dentin as well as on artificial dentin caries-like lesions. Methods: Bovine dentin specimens (n = 240), with 2 different surfaces each (1 sound surface [sound treatment (ST)] and one caries lesion [demineralized treatment (DT)]), were prepared and randomly allocated to one highly (6 × 120 min demineralization/day [H]) and one lowly cariogenic (6 × 60 min demineralization/day [L]) pH-cycling model. Treatments during pH-cycling (28 days) were: brushing 2×/ day with: 0 ppm F [H0/L0], 1,450 ppm F [H1,450/L1,450], 2,800 ppm F [H2,800/L2,800], 5,000 ppm F [H5,000/L5,000], 5,000 ppm F plus TCP [H5,000+TCP/L5,000+TCP], and 12,500 ppm F [H12,500/ L12,500] containing dentifrices/gels. Dentifrice/gel slurries were prepared with deionized water (1: 2 wt/wt). Differences in integrated mineral loss (ΔΔZ) and Δ lesion depth were calculated between values before and after pH-cycling using transversal microradiography. Results: The correlation between ΔΔZDT and F– was strong for the highly (rH = 0.691; p < 0.001) and moderate (rL = 0.500; p < 0.001) for the lowly cariogenic model, indicating a fluoride dose-response for both. Significant differences for ΔΔZDT and ΔΔZST could be found between H0, H1,450, H5,000, and H12,500 as well as L0, L5,000, and L125,000 (p ≤ 0.046; analysis of covariance [ANCOVA]). Except for 0 ppm F–, no significant difference in ΔΔZST and ΔΔZDT could be found between the highly and lowly cariogenic model (p ≥ 0.056; ANCOVA). Conclusion: For both pH-cycling conditions a dose-response for fluoride could be revealed. For elderly people with exposed root surfaces, the use of gels containing 12,500 ppm F instead of regularly (1,450 ppm F) or highly (5,000 ppm F) fluoridated dentifrices should be further investigated, as it offered higher cariespreventive effects in vitro

Demineralization Inhibitory Effects of Highly Concentrated Fluoride Dentifrice and Fluoride Gels/Solutions on Sound Dentin and Artificial Dentin Caries Lesions in vitro

Objectives: The aim of this in vitro study was to compare the demineralization inhibitory effect of gels/solutions used in combination with either standard or highly fluoridated dentifrices on sound dentin as well as on artificial dentin carieslike lesions. Methods: Bovine dentin specimens (n = 240) with two different surfaces each (sound [ST] and artificial caries lesion [DT]) were prepared and randomly allocated to twelve groups. Weekly interventions during pH-cycling (28 days, 6 × 120 min demineralization/day) were: the application of gels/solutions containing amine fluoride/sodium fluoride (12,500 ppm F [ppm]; pH = 4.4; AmF); NaF (12,500 ppm; pH = 6.6; NaF1); NaF (12,500 ppm; pH = 6.3; NaF2); silver diamine fluoride (14,200 ppm; pH = 8.7; SDF); acidulated phosphate fluoride (12,500 ppm; pH = 3.8; APF), and no intervention (standard control; S). Furthermore, half of the specimens in each group were brushed (10 s; twice per day) with dentifrice slurries containing either 1,450 ppm (e.g., AmF1450) or 5,000 ppm (e.g., AmF5000). Differences in integrated mineral loss (ΔΔZ) and lesion depth (ΔLD) were calculated between values before and after pH-cycling using transversal microradiography. Results: After pH-cycling Ss showed significantly increased ΔZDT and LDDT values, indicating further demineralization. In contrast, except for one, all groups including fluoride gels/solutions showed significantly decreased ΔZDT values. Additional use of most fluoride gels/solutions significantly enhanced mineral gain, mainly in the surface area; however, acidic gels/solutions seemed to have negative effects on lesion depths. Significance: Under the present pH-cycling conditions the highly fluoridated dentifrice significantly reduced caries progression and additional application of nearly all of the fluoride gels/solutions resulted in remineralization. However, there was no difference in the remineralizing capacity of fluoride gels/solutions when used in combination with either standard or highly fluoridated dentifrices

Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning.

Convolutional Neural Networks (CNNs) such as U-Net have been widely used for medical image segmentation. Dental restorations are prominent features of dental radiographs. Applying U-Net on the panoramic image is challenging, as the shape, size and frequency of different restoration types vary. We hypothesized that models trained on smaller, equally spaced rectangular image crops (tiles) of the panoramic would outperform models trained on the full image. A total of 1781 panoramic radiographs were annotated pixelwise for fillings, crowns, and root canal fillings by dental experts. We used different numbers of tiles for our experiments. Five-times-repeated three-fold cross-validation was used for model evaluation. Training with more tiles improved model performance and accelerated convergence. The F1-score for the full panoramic image was 0.7, compared to 0.83, 0.92 and 0.95 for 6, 10 and 20 tiles, respectively. For root canals fillings, which are small, cone-shaped features that appear less frequently on the radiographs, the performance improvement was even higher (+294%). Training on tiles and pooling the results thereafter improved pixelwise classification performance and reduced the time to model convergence for segmenting dental restorations. Segmentation of panoramic radiographs is biased towards more frequent and extended classes. Tiling may help to overcome this bias and increase accuracy

Segmentation of dental restorations on panoramic radiographs using deep learning

Convolutional Neural Networks (CNNs) such as U-Net have been widely used for medical image segmentation. Dental restorations are prominent features of dental radiographs. Applying U-Net on the panoramic image is challenging, as the shape, size and frequency of different restoration types vary. We hypothesized that models trained on smaller, equally spaced rectangular image crops (tiles) of the panoramic would outperform models trained on the full image. A total of 1781 panoramic radiographs were annotated pixelwise for fillings, crowns, and root canal fillings by dental experts. We used different numbers of tiles for our experiments. Five-times-repeated three-fold cross-validation was used for model evaluation. Training with more tiles improved model performance and accelerated convergence. The F1-score for the full panoramic image was 0.7, compared to 0.83, 0.92 and 0.95 for 6, 10 and 20 tiles, respectively. For root canals fillings, which are small, cone-shaped features that appear less frequently on the radiographs, the performance improvement was even higher (+294%). Training on tiles and pooling the results thereafter improved pixelwise classification performance and reduced the time to model convergence for segmenting dental restorations. Segmentation of panoramic radiographs is biased towards more frequent and extended classes. Tiling may help to overcome this bias and increase accuracy

Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs-A Retrospective Study.

We aimed to assess the effects of hyperparameter tuning and automatic image augmentation for deep learning-based classification of orthodontic photographs along the Angle classes. Our dataset consisted of 605 images of Angle class I, 1038 images of class II, and 408 images of class III. We trained ResNet architectures for classification of different combinations of learning rate and batch size. For the best combination, we compared the performance of models trained with and without automatic augmentation using 10-fold cross-validation. We used GradCAM to increase explainability, which can provide heat maps containing the salient areas relevant for the classification. The best combination of hyperparameters yielded a model with an accuracy of 0.63-0.64, F1-score 0.61-0.62, sensitivity 0.59-0.65, and specificity 0.80-0.81. For all metrics, it was apparent that there was an ideal corridor of batch size and learning rate combinations; smaller learning rates were associated with higher classification performance. Overall, the performance was highest for learning rates of around 1-3 × 10-6 and a batch size of eight, respectively. Additional automatic augmentation improved all metrics by 5-10% for all metrics. Misclassifications were most common between Angle classes I and II. GradCAM showed that the models employed features relevant for human classification, too. The choice of hyperparameters drastically affected the performance of deep learning models in orthodontics, and automatic image augmentation resulted in further improvements. Our models managed to classify the dental sagittal occlusion along Angle classes based on digital intraoral photos

Randomized in situ evaluation of surface polishing protocols on the caries-protective effect of resin Infiltrant.

The aim of this placebo-controlled randomized in situ study was to evaluate the effect of different surface polishing protocols on enamel roughness, bacterial adhesion and caries-protective effect of a resin infiltrant. Seventy-five bovine enamel samples having artificial caries lesions were treated with a resinous infiltrant and afterwards randomly dividided into five polishing protocols: aluminum oxide flexible disks (Al2O3-Disks), silicon carbide tips (SIC-Tips), silicon carbide brush (SIC-Brush), silicon carbide polyester strips (SIC-Strips) or no polishing [negative control (NC)]. Average surface roughness (Ra) was assessed by profilometry. Samples were mounted in palatal appliances under a mesh for biofilm accumulation. Fifteen volunteers wore the intraoral appliances (14-days) and cariogenic challenge was triggered by sucrose solutions. Biofilm formed was collected for microbiological analysis of caries-related bacteria (Streptococcus mutans, Lactobacillus acidophilus) and demineralization was assessed by cross-sectional microhardness. Mean Knoop hardness numbers (Kg/mm2) were plotted over lesion depth (µm) and area under the lesion curve was subtracted from sound enamel to determine demineralization (ΔS, Kg/mm2xµm). Data were analyzed by ANOVA and post-hoc comparisons (α = 0.05). NC resulted in significantly higher Ra means than Al2O3-Disks and SIC-Strips. Bacterial counts were not significantly different between the groups (p > 0.05). Regards ΔS means, however none of the groups were significantly different to NC (6983.3 kg/mm2xµm /CI 4246.1-9720.5, p > 0.05). Conclusions: Polishing protocols (Al2O3-Disks, SIC-Strips) significantly decreseased roughness of infiltrated-enamel, however none of the polishing protocols could signicantly decrease bacterial counts nor resulted in significant less demineralization

Longevity of immediate rehabilitation with direct fiber reinforced composite fixed partial dentures after up to 9 years.

OBJECTIVES This retrospective, single-center, practice-based cohort study aimed to analyze the longevity of direct fiber reinforced composite fixed partial dentures (DFRC-FPD) and to analyze factors influencing their survival and success. METHODS Within one private practice 100 DFRC-FPD were directly applied. The preparation of a proximal cavity was limited to abutment teeth with an existing filling (minimal-invasive approach). All intact enamel surfaces were preserved (micro-invasive approach). DFRC-FPD were reinforced by fiber-splints with semi polymer network matrices (Everstick C + B©). At the last follow-up DFRC-FPD were considered successful if they were still in function without any need of therapy. DFRC-FPD were considered as survived if they were repaired or replaced. Multi-level Cox proportional hazard models were used to evaluate the association between clinical factors and time. RESULTS Within a mean follow-up period (range) of 53 (2-109) months 7 bridges did not survive (cumulative survival rate: 93%) and further 24 bridges had received a restorative follow-up treatment (cumulative success rate: 69%). The annual failure rate was 1.6% for survival and 8.3% for success. The main failure type was fracture of the composite material (n = 30). In multivariate analysis no significant predictor could be found for success and survival. CONCLUSIONS For directly prepared fiber reinforced composite bridges high survival and moderate success times were observed after up to nine years. Based on the present results DFRC-FPD might be an immediate, short- to medium-term solution for replacing 1 to 2 missing teeth with no or minimal tooth preparation. CLINICAL SIGNIFICANCE Within the limitations of the present study DFRC-FPD offered an immediate, micro-/minimal-invasive, inexpensive short- and medium-term solution to replace missing teeth, even if no box-shaped proximal cavity was prepared