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Aim: To propose a framework for consistently applying the 2018 periodontal status classification scheme to epidemiological surveys (Application of the 2018 periodontal status Classification to Epidemiological Survey data, ACES)., Proposed Framework: We specified data requirements and workflows for either completed or planned epidemiological surveys, utilizing commonly collected measures of periodontal status (clinical attachment levels [CAL], probing depths, bleeding on probing), as well as additional necessary variables for the implementation of the 2018 periodontal status classification (tooth loss due to periodontitis and complexity factors). Following detailed instructions and flowcharts, survey participants are classified as having periodontal health, gingivitis or periodontitis. Rates of edentulism must also be reported. In cases of periodontitis, instructions on how to compute the stage and extent are provided. Assessment of grade can be derived from CAL measurements (or from radiographic alveolar bone loss data) in relation to root length and the participant's age., Conclusions: ACES is a framework to be used in epidemiological studies of periodontal status that (i) have been completed, and in which stage and grade according to the 2018 classification are inferred retroactively, or (ii) are being planned. Consistent use of the proposed comprehensive approach will facilitate the comparability of periodontitis prevalence estimates across studies., (© 2024 The Authors. Journal of Clinical Periodontology published by John Wiley & Sons Ltd.)

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: To predict patients' tooth loss during supportive periodontal therapy across four German university centers., Methods: Tooth loss in 897 patients in four centers (Kiel (KI) n = 391; Greifswald (GW) n = 282; Heidelberg (HD) n = 175; Frankfurt/Main (F) n = 49) during supportive periodontal therapy (SPT) was assessed. Our outcome was annualized tooth loss per patient. Multivariable linear regression models were built on data of 75 % of patients from one center and used for predictions on the remaining 25 % of this center and 100 % of data from the other three centers. The prediction error was assessed as root-mean-squared-error (RMSE), i.e., the deviation of predicted from actually lost teeth per patient and year., Results: Annualized tooth loss/patient differed significantly between centers (between median 0.00 (interquartile interval: 0.00, 0.17) in GW and 0.09 (0.00, 0.19) in F, p = 0.001). Age, smoking status and number of teeth before SPT were significantly associated with tooth loss (p < 0.03). Prediction within centers showed RMSE of 0.14-0.30, and cross-center RMSE was 0.15-0.31. Predictions were more accurate in F and KI than in HD and GW, while the center on which the model was trained had a less consistent impact. No model showed useful predictive values., Conclusion: While covariates were significantly associated with tooth loss in linear regression models, a clinically useful prediction was not possible with any of the models and generalizability was not given. Predictions were more accurate for certain centers., Clinical Relevance: Association should not be confused with predictive value: Despite significant associations of covariates with tooth loss, none of our models was useful for prediction. Usually, model accuracy was even lower when tested across centers, indicating low generalizability., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: We aimed to assess periodontal services utilization in very old Germans., Methods: A comprehensive sample of very old (≥ 75 years), insured at a large Northeastern statutory insurer was followed over 6 years (2012-2017). We assessed periodontal service provision, entailing (1) periodontal screening index (PSI), (2) periodontal status/treatment planning, (3) periodontal therapy (scaling and root planning with or without access surgery), (4) postoperative reevaluation, and (5) any of these four services groups. Association of utilization with (1) sex, (2) age, (3) region, (4) social hardship status, (5) ICD-10 diagnoses, and (6) diagnoses-related groups was explored., Results: 404.610 individuals were followed; 173,733 did not survive follow-up. The mean (SD) age was 81.9 (5.4) years. 29.4% (119,103 individuals) utilized any periodontal service, nearly all of them the PSI. Periodontal status/treatment planning, treatment provision, and reevaluation were provided to only a small fraction (1.54-1.57%, or 6224-6345) of individuals. The utilization of the PSI increased between 2012 and 2017; no such increase was observed for treatment-related services. Utilization decreased with age; those aged > 85 years received nearly no services at all. Decreases were more pronounced for treatment-related services. Utilization was lower in rural than urban areas, those with hardship status, and those severely ill (e.g., dementia, heart insufficiency). In multivariable analysis, a previous PSI measurement tripled the odds of receiving treatment-related services (OR: 3.2; 95% CI: 3.0-3.4)., Conclusions: Periodontal services utilization was low. Screening for periodontal disease significantly increased therapy provision. Social, demographic, regional, and general health aspects were associated with utilization., Clinical Significance: The utilization of periodontal services in the very old in Northeast Germany was low, and even screening was only performed in a minority of individuals. Policies to increase identification and management of periodontitis especially in the most vulnerable individuals are needed.

Objectives: To assess if long-term treatment costs in periodontitis patients differ between stage III vs. IV and grade B vs. C according to the 2018 classification of periodontal diseases., Methods: A cohort of 231 periodontitis patients (followed over a mean of 18.4 years) was evaluated. Costs for active periodontal therapy (APT, including scaling and root planning, open flap debridement, root resections) and supportive periodontal therapy (SPT, including also restorative, endodontic, prosthetic and surgical treatment) were estimated from a mixed payer-perspective in Germany (in Euro 2020). Multi-dimensional staging and grading was applied. The impact of stage, grade, sex and age on total and annual costs was assessed., Results: Mean (SD) total and annual treatment costs were 7154 (2554) Euro and 437 (222) Euro. Costs were generated during SPT (92 %) and by periodontal treatment (88 %) and decreased significantly with longer follow-up (p < 0.001). Total costs were 7120 (2692) Euro in stage III (n = 154) vs. 7221 (2271) Euro in stage IV (n = 77; p > 0.05), and 6256 (1605) Euro in grade B (n = 35) vs. 7314 (2660) Euro in grade C (n = 196; p < 0.001). Annual costs were 426 (219) Euro vs. 459 (228) Euro for stage III vs. stage IV (p > 0.05) and 308 (163) Euro vs. 460 (224) Euro for grade B vs. grade C (p < 0.001). Multivariable modelling found grade, but not stage, sex and age significantly associated with costs., Conclusions: Within the limitations of this study, and in patients with severe periodontitis who were systematically treated long-term, grading, but not staging was associated with costs., Clinical Significance: Treatment costs were higher in patients with more progressive periodontitis and were found to decrease during follow-up. Dentists need to consider costs during treatment planning and communication with patients., Competing Interests: Declaration of Competing Interest We have no competing interests., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Aim: A range of predictors for tooth loss in periodontitis patients have been reported. We performed a systematic review and meta-analysis to assess the consistency and magnitude of any association between a total of 12 predictors and tooth loss., Materials and Methods: Medline/Embase/Central were searched for longitudinal studies investigating the association between predictors and tooth loss in periodontitis patients. Random-effects meta-analysis was performed, and study quality assessed., Results: Twenty studies (15,422 patients, mean follow-up: 12 years) were included. The mean annual tooth loss/patient was 0.12 (min./max: 0.01/0.36). Older patients (n = 8 studies; OR: 1.05, 95% CI: 1.03-1.08/year), non-compliant ones (n = 11; 1.51, 1.06-2.16), diabetics (n = 7; 1.80, 1.26-2.57), those with IL-1-polymorphism (n = 3; 1.80; 1.29-2.52) and smokers (n = 15; 1.98, 1.58-2.48) had a significantly higher risk of tooth loss. Teeth with bone loss (n = 3; 1.04, 1.03-1.05/%), high probing pocket depth (n = 6; 3.19, 1.70-5.98), mobility (n = 4; 3.71, 1.65-8.38) and molars (n = 4; 4.22, 2.12-8.39), especially with furcation involvement (n = 5; 2.68, 1.75-4.08) also showed higher risks. Gender (n = 16; 0.95, 0.86-1.05) and endodontic affection (n = 3; 3.62, 0.99-13.2) were not significantly associated with tooth loss., Conclusions: Older, non-compliant, smoking or diabetic patients, and teeth with bone loss, high probing pocket depth, mobility, or molars, especially with furcation involvement showed higher risks of tooth loss., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)