Peripheral calcifying odontogenic cyst: a case report and comprehensive review of 60 years of literature.

Peripheral Calcifying Odontogenic Cyst (PCOC) is the extraosseous form of calcifying odontogenic cyst that is limited to peripheral soft tissue without bony involvement. This case report presents a case of PCOC manifested as a progressive growth of gingival mass in a young male treated with excisional biopsy. Histological examination confirmed diagnosis of PCOC with presence of characteristic ghost cells and sporadic calcifications. No recurrence of the lesion and no complication were noted at three-year follow-up. Review of available literature on PCOC noted a predilection of occurrence in the mandible (61%) and in the anterior area of the jaws (58%). Mean age of patients was 41.7 ± SD25.43 (7-83) and 95% CI [33.6, 49.8] yrs. Mean size of the lesions was 1.38 ± SD1.1 (0.5-4.3) and 95% CI [0.93, 1.83] cm. Gender distribution was noted to be 51.3% male and 48.7% female.

Ridge preservation with the socket-shield technique and immediate provisionalization for delayed implant placement.

AIM: The present case report with medium-term follow-up aims to report on the successful outcomes of ridge preservation using anorganic bovine bone mineral (ABBM) combined with the socket-shield technique (SST) in conjunction with immediate ovate pontic provisionalization of the extraction site, followed by delayed flapless implant placement and immediate implant provisionalization in the rehabilitation of the maxillary anterior esthetic region. Clinical protocol: Three consecutive patients with failing maxillary anterior teeth were treated with this protocol. The alveolar ridge dimension was preserved utilizing ABBM, combined with the retention of a partial tooth root segment in the buccal portion of the extraction socket, to minimize ridge resorption following tooth extraction. An immediate fixed provisional restoration with an ovate base in direct contact with the extraction site was fabricated and delivered immediately to maintain the soft tissue contour. At 4 months postextraction, flapless implant placement was performed via a computer-generated surgical guide, along with immediate implant provisionalization. Definitive restorations were delivered 4 months following implant placement. RESULTS: At the 2-year follow-up after loading, prosthetic and implant success was demonstrated, with a favorable esthetic outcome and a high level of patient satisfaction. CONCLUSIONS: The immediate ovate pontic provisionalization in conjunction with ridge preservation combining ABBM and the SST, followed by flapless computer-guided implant placement with an immediate implant provisionalization protocol, as proposed in this article, offers a viable method to minimize alveolar ridge resorption as well as optimize esthetic outcomes.

Identification of microbiological factors associated with periodontal health disparities.

The present study aimed at identifying risk factors associated with periodontitis development and periodontal health disparities with emphasis on differential oral microbiota. The prevalence of periodontitis is recently rising dentate adults in the US, which presents a challenge to oral health and overall health. The risk of developing periodontitis is higher in African Americans (AAs), and Hispanic Americans (HAs) than in Caucasian Americans (CAs). To identify potentially microbiological determinations of periodontal health disparities, we examined the distribution of several potentially beneficial and pathogenic bacteria in the oral cavities of AA, CA, and HA study participants. Dental plaque samples from 340 individuals with intact periodontium were collected prior to any dental treatment, and levels of some key oral bacteria were quantitated using qPCR, and the medical and dental histories of participants were obtained retrospectively from axiUm. Data were analyzed statistically using SAS 9.4, IBM SPSS version 28, and R/RStudio version 4.1.2. Amongst racial/ethnic groups: 1) neighborhood medium incomes were significantly higher in the CA participants than the AA and the HA participants; 2) levels of bleeding on probing (BOP) were higher in the AAs than in the CAs and HAs; 3) Porphyromonas gingivalis levels were higher in the HAs compared to that in the CAs; 4) most P. gingivalis detected in the AAs were the fimA genotype II strain that was significantly associated with higher BOP indexes along with the fimA type IV strain. Our results suggest that socioeconomic disadvantages, higher level of P. gingivalis, and specific types of P. gingivalis fimbriae, particularly type II FimA, contribute to risks for development of periodontitis and periodontal health disparities.

Application of deep machine learning for the radiographic diagnosis of periodontitis.

OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of radiographic bone loss (RBL) in diagnosing and treatment planning for periodontitis. This study aimed to test the accuracy of RBL classification by machine learning. MATERIALS AND METHODS: A total of 236 patients with standardized full mouth radiographs were included. Each tooth from the periapical films was evaluated by three calibrated periodontists for categorization of RBL and radiographic defect morphology. Each image was pre-processed and augmented to ensure proper data balancing without data pollution, then a novel multitasking InceptionV3 model was applied. RESULTS: The model demonstrated an average accuracy of 0.87 ± 0.01 in the categorization of mild (< 15%) or severe (≥ 15%) bone loss with fivefold cross-validation. Sensitivity, specificity, positive predictive, and negative predictive values of the model were 0.86 ± 0.03, 0.88 ± 0.03, 0.88 ± 0.03, and 0.86 ± 0.02, respectively. CONCLUSIONS: Application of deep machine learning for the detection of alveolar bone loss yielded promising results in this study. Additional data would be beneficial to enhance model construction and enable better machine learning performance for clinical implementation. CLINICAL RELEVANCE: Higher accuracy of radiographic bone loss classification by machine learning can be achieved with more clinical data and proper model construction for valuable clinical application.

Use of the deep learning approach to measure alveolar bone level.

AIM: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis. MATERIALS AND METHODS: A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners. RESULTS: The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners ( p=.65 ). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85. CONCLUSIONS: The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.

Potential Microbiological Risk Factors Associated With Periodontitis and Periodontal Health Disparities.

Periodontitis disproportionately affects different racial and ethnic populations. In this study, we used qPCR to determine and compare oral microbial profiles in dental plaque samples from 191 periodontitis patients of different ethnic/racial backgrounds. We also obtained the periodontal parameters of these patients retrospectively using axiUm and performed statistical analysis using SAS 9.4. We found that in this patient cohort, neighborhood median incomes were significantly higher among Caucasians Americans (CAs) than among African Americans (AAs) and Hispanic Americans (HAs). Levels of total bacteria and Porphyromonas gingivalis, a keystone periodontal pathogen, were not evenly distributed among the three groups. We confirmed our previous findings that Streptococcus cristatus reduces P. gingivalis virulence potential and likely serves as a beneficial bacterium. We also showed the ratio of S. cristatus to P. gingivalis to be significantly higher in CAs than in HAs and AAs. Our results suggest that higher levels of P. gingivalis and lower ratios of S. cristatus to P. gingivalis may contribute to periodontal health disparities.

The Ball Technique for Peri-implant Soft Tissue Augmentation Using a Xenogeneic Collagen Matrix: A Technical Report.

In the maxillary anterior region, augmentation to correct a soft tissue deficiency is often required for an esthetic outcome and long-term implant therapy success. This case series of three patients presents a novel approach for soft tissue augmentation using xenogeneic collagen matrix balls in the esthetic zone around the implants. This technique avoids a secondary donor site compared to autogenous connective tissue graft. With this technique, a horizontal soft tissue volume increase (range: 3 to 5 mm) was observed postsurgically and maintained at later follow-ups. The described ball technique offers a viable method for peri-implant mucosal augmentation in the maxillary anterior region.