Periodontal grading-estimation of responsiveness to therapy and progression of disease.

OBJECTIVE: To investigate the capability of periodontal grading to estimate the progression of periodontal disease and the responsiveness to therapy. MATERIALS AND METHODS: Eighty-four patients who underwent non-surgical therapy (NST) were included. Direct and indirect evidence of progression were determined according to the current classification. Responsiveness to therapy was examined using mean pocket probing depths reduction (PPDRed), reduction of bleeding on probing (BOPRed), and the rate of pocket closure (%PC) after six months. RESULTS: Statistical analysis revealed no agreement between direct and indirect evidence in grading periodontitis (κ = 0.070). The actual rate of progression as determined by longitudinal data was underestimated in 13% (n = 11), overestimated in 51% (n = 43) and correctly estimated in 30% (n = 36) by indirect evidence. No significant differences in responsiveness to therapy were observed in patients graded according to direct evidence. Using indirect evidence, patients assigned grade C showed more PPDRed but less BOPRed and lower %PC compared to grade B. CONCLUSION: The present data indicate that indirect evidence may lead to inaccuracies compared to direct evidence regarding the estimation of periodontal progression. However, indirect evidence seems to be more suitable in the estimation of responsiveness to therapy than direct evidence, helping to identify cases that are more likely to require additional therapies such as re-instrumentation or periodontal surgery. CLINICAL RELEVANCE: Regarding the estimation of disease progression and responsiveness to periodontal therapy, accuracy and reliability of both direct and indirect evidence are limited when grading periodontitis.

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks.

Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.

Detection of Periodontal Bone Loss on Periapical Radiographs-A Diagnostic Study Using Different Convolutional Neural Networks.

Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.

Influence of decision-making algorithms on the diagnostic accuracy using the current classification of periodontal diseases-a randomized controlled trial.

OBJECTIVES: To examine the influence of the decision-making algorithms published by Tonetti and Sanz in 2019 on the diagnostic accuracy in two differently experienced groups of dental students using the current classification of periodontal diseases. MATERIALS AND METHODS: Eighty-three students of two different clinical experience levels were randomly allocated to control and study group, receiving the staging and grading matrix, resulting in four subgroups. All diagnosed two patient cases with corresponding periodontal charts, panoramic radiographs, and intraoral photographs. Both presented severe periodontal disease (stage III, grade C) but considerably differed in complexity and phenotype according to the current classification of periodontal diseases. Controls received the staging and grading matrix published within the classification, while study groups were additionally provided with decision-trees published by Tonetti and Sanz. Obtained data was analyzed using chi-square test, Spearman's rank correlation, and logistic regression. RESULTS: Using the algorithms significantly enhanced the diagnostic accuracy in staging (p = 0.001*, OR = 4.425) and grading (p < 0.001**, OR = 30.303) regardless of the clinical experience. In addition, even compared to the more experienced control, less experienced students using algorithms showed significantly higher accuracy in grading (p = 0.020*). No influence on the criteria extent could be observed comparing study groups to controls. CONCLUSION: The decision-making algorithms may enhance diagnostic accuracy in dental students using the current classification of periodontal diseases. CLINICAL RELEVANCE: The investigated decision-making algorithms significantly increased the diagnostic accuracy of differently experienced under graduated dental students and might be beneficial in periodontal education.

Disrupted biomineralization in zebra mussels after exposure to bisphenol-A: Potential implications for molar-incisor hypomineralization.

OBJECTIVE: The aetiology of molar-incisor hypomineralization (MIH) is currently unclear. A major hurdle in MIH research is the lack of adequate model systems. The study investigated the feasibility of zebra mussel (Dreissena polymorpha) as a novel model to screen potential MIH-related factors. METHODS: In four experiments with overall 46 groups (n = 7 mussels/group), six groups per experiment were incubated with 100 mg/l calcein (mineralization marker) solution for 96 h to evaluate the dynamics of shell biomineralization, another six groups with tap water only (controls). Then zebra mussels with and without calcein pre-incubation were exposed to cadmium sulfate hydrate (3CdSO4•8H2O) (positive control; 0, 0.01, 0.1, 1, 10 and 100 mg/l), possible aetiological factors of MIH including bisphenol-A (BPA; 0, 0.02, 0.2, 2, 20 and 200 mg/l) and erythromycin (0, 0.1, 1, 10, 100 and 1000 mg/l) as mineralization "disruptors", and doxycycline (0, 0.1, 1, 10, 100 and 1000 mg/l) for 96 h, respectively. After two weeks, the mussels were sacrificed and shells were embedded in methylmethacrylate for fluorescence intensity analysis. RESULTS: Mortality rate was 100% after 20, 200 mg/l BPA and 10, 100 mg/l 3CdSO4•8H2O exposure. Thereby, the median lethal concentration (96 h-LC50) of BPA was 6.3 mg/l (95% CI, 1.3-34.4 mg/l), and that of cadmium was 3.1 mg/l (95% CI, 0.7-10.5 mg/l). Notably, calcein fluorescence in shells significantly decreased (p < 0.05) after 2 mg/l BPA and 1 mg/l 3CdSO4•8H2O exposure. SIGNIFICANCE: These findings suggest that BPA may disrupt biomineralization. Biomineralization in zebra mussels seems to be an effective model for investigating potential MIH-related factors.

Distribution and Polarization of Caries in Adolescent Populations.

The aim of this study was to determine the proportion of adolescents with severe caries to analyze the prevalence of caries and to visualize the unequal distribution. Data from three epidemiological studies (10- and 15-year-olds: GINIplus and LISA cohorts in Munich, Bavaria; 12-year-olds: LAGZ survey in Bavaria, Germany) with 2875 adolescents were available for analysis. All individuals were examined according to the WHO standard. Statistics included the calculation of mean dmft/DMFT values (standard deviation), Significant Caries Index (SiC) values, Specific Affected Caries Index (SaC) values, and Lorenz curves. Overall caries-free status was 58.6% in primary and 83.9% in secondary teeth (10-year-olds), 61.5% (12-year-olds), and 64.6% (15-year-olds). The proportion of 12- and 15-year-olds with at least four DMFTs was 9.4% and 8.3%, respectively. In addition, eight 15-year-olds with DMFT values ≥8 (0.6%) were registered. The SaC/SiC values amounted to 1.8/0.9 DMFT (10-year-olds), 2.6/2.8 DMFT (12-year-olds), and 2.5/2.5 DMFT (15-year-olds). The mean DMFT values in the upper 1% of subjects were 4.2 DMFT (10-year-olds), 8.5 DMFT (12-year-olds), and 8.5 DMFT (15-year-olds). Thus, caries is not equally distributed throughout adolescence, but individuals with severe caries are rare. Nevertheless, further interdisciplinary research seems to be needed to clarify potential risk factors.

Remineralization, Regeneration, and Repair of Natural Tooth Structure: Influences on the Future of Restorative Dentistry Practice.

Currently, the principal strategy for the treatment of carious defects involves cavity preparations followed by the restoration of natural tooth structure with a synthetic material of inferior biomechanical and esthetic qualities and with questionable long-term clinical reliability of the interfacial bonds. Consequently, prevention and minimally invasive dentistry are considered basic approaches for the preservation of sound tooth structure. Moreover, conventional periodontal therapies do not always ensure predictable outcomes or completely restore the integrity of the periodontal ligament complex that has been lost due to periodontitis. Much effort and comprehensive research have been undertaken to mimic the natural development and biomineralization of teeth to regenerate and repair natural hard dental tissues and restore the integrity of the periodontium. Regeneration of the dentin-pulp tissue has faced several challenges, starting with the basic concerns of clinical applicability. Recent technologies and multidisciplinary approaches in tissue engineering and nanotechnology, as well as the use of modern strategies for stem cell recruitment, synthesis of effective biodegradable scaffolds, molecular signaling, gene therapy, and 3D bioprinting, have resulted in impressive outcomes that may revolutionize the practice of restorative dentistry. This Review covers the current approaches and technologies for remineralization, regeneration, and repair of natural tooth structure.