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.

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.

Bemcentinib is a newly developed AXL inhibitor that is currently under investigation in phase II trails for the treatment of acute myeloblastic leukemia (AML). Clinical and radiographic findings in this case were very similar to cases of MRONJ in patients receiving Sunitinib or other anti-angiogenetic substances, assuming that Bemcentinib may cause similar oral side effects. We present a male 81-year-old patient with a manifestation of alveolar bone necrosis at the central upper incisors following a 2-month regimen with the AXL-inhibitor Bemcentinib, administered for the treatment of secondary acute myeloblastic leukemia (sAML). Due to the duration of less than 8 weeks, the osteonecrosis was diagnosed as necrotizing periodontitis, but the intraoral clinical and radiographic findings were also compatible with the differential diagnosis of medication-related osteonecrosis of the jaw (MRONJ, stage II). Following to discontinuation of Bemcentinib, the affected bone was surgically revised including the removal of a demarcated bone sequester under preventive antibiotic treatment (metronidazole 400 mg t.i.d.). We hypothesize that Bemcentinib might increase the susceptibility for osteonecrosis of the jaw, probably related to its antiangiogenic effects and the resulting modulation of host immune response. Based on the current observations, it can be assumed that oro-dental health might be significant also prior and during treatment with Bemcentinib for the prevention of MRONJ.

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.

Objectives: Roughly one in eight individuals presents with psychiatric disorders which were proposed to significantly affect oral health. This study compared oral health of 112 patients (mean 28 years), 31 with schizophrenia spectrum disorders (SSD) or 33 with major depression/bipolar disorders (MDD/BD) to 52 healthy controls (HCG). Materials and methods: Oral health parameters, including caries experience (decayed-missed-filled teeth/surfaces-index DMFT/DMFS), the presence of plaque (plaque-index PI) and periodontal health (bleeding on probing BOP, periodontal probing depths PPD%), were evaluated by examiners blinded to psychiatric diagnoses. Descriptive statistics summarized demographic and clinical data. Group differences and associations with dental outcomes were analyzed using Chi-square or Mann-Whitney-U tests. Logistic regression identified predictors of oral health. Results: Patients with SSD or MDD/BD resented with significantly oorer oral health than HCG regarding DMFT (SSD = 9; MDD/BD = 10; HCG = 2), DMFS (SSD = 10; MDD/BD = 12; HCG = 1), PI (SSD = 2; MDD/BD = 2; HCG = 1), BOP (SSD = 20%; MDD/BD = 17%; HCG = 3)% and PPD% (SSD = 1%; MDD/BD = 0%; HCG = 0%) and smoked more often. Differences regarding dental anxiety were not significant (p = 0.112). Subgroup analysis showed no differences between SSD and MDD/BD. Conclusions: SSD and MDD/BD and smoking are key contributors to poor dental health shown by significantly worse DMFT, DMFS and PI, BOP. Probably this might additionally be enhanced by concomitant medication, with multiple psychiatric medication being associated with poorer oral health regarding DMFT, PI and BOP. Clinical relevance: Routine dental care and personalized oral hygiene training to address disease-specific risks are desirable for individuals with SSD or MDD/BD.

Background: Refugee families often arrive in Europe full of hope after facing many challenges. It is known that a significant proportion of them suffer from post-traumatic stress disorder (PTSD) and that this is associated with bad health status, including oral health in adults. While it is known that refugee children have poor dental health, it remains unclear if this is related to their mental health. We aimed to find out if children’s PTSD status was associated with their dental health. We further examined if parental mental health is associated with children’s dental health. Methods: The study is part of the German INterCuLtUral Child DevelopmEnt Studies (INCLUDE). Dental and psychological examination of n = 44 children between 1.5 and 6 years was conducted. Dental health was assessed with dmft value and O’Leary Plaque Index (PI). The Child and Adolescent Trauma Screening was applied to test for PTSD symptoms in children. Parental mental health was assessed with the refugee health screener (RHS). Descriptive analyses and multiple linear regression were performed to test the association between child’s PTSD and dental health with parental mental health. Results: Mean dmft value was 4.98 (± 3.69), mean PI was 40.92 (± 14.76). PTSD was detected in 30% of the children. There were no statistically significant differences between children with and without PTSD symptoms pertaining dental health. Linear regression yielded child’s age and parental RHS score to be positively associated with child’s dmft value. Conclusions: Children’ had poor dental health status, regardless of the presence of PTSD. Parental mental health was associated with child dental health. Interventions and programs for refugee children should consider parental mental health as well.