Primary bone augmentation leads to equally stable marginal tissue conditions comparing the use of xenograft blocks infused with BMP-2 and autogenous bone blocks: A 3D analysis after 3 years

Objectives

To test whether or not primary bone augmentation using xenograft blocks infused with BMP‐2 or autogenous bone blocks lead to similar results regarding the implant survival and 3D marginal soft tissue contours.

Methods

Twenty‐four patients with an insufficient ridge width for implant placement in need of primary augmentation were randomly assigned to either a block of deproteinized bovine bone mineral infused with rhBMP‐2 (BMP) or an intraorally harvested block of autogenous bone (ABB). At 4 months, 1–4 dental implants were placed in the regenerated area. After crown insertion and at 3 years, peri‐implant tissue parameters, two‐ and three‐dimensional radiographic parameters, and soft tissue contour changes were evaluated. Explorative mixed model analyses were performed. The level of significance was set at 5%.

Results

At the 3‐year follow‐up, 23 patients with 40 implants were evaluated. The implant survival rate was 100% in both groups. At baseline, the marginal hard tissue levels amounted to −0.4 ± 0.8 mm (mean ± standard deviation) in the BMP group and −0.7 ± 1.0 mm in the ABB group. At 3 years, these values were −0.2 ± 0.4 mm (BMP) and −0.6 ± 1.0 mm (ABB). At baseline, the thickness of the buccal hard tissue at the level of the implant shoulder measured 1.1 ± 1.1 mm (BMP) and 1.4 ± 1.0 mm (ABB). At 3 years, it measured 0.9 ± 0.9 mm (BMP) and 0.7 ± 0.6 mm (ABB).

Conclusions

The present study demonstrated excellent implant survival rates and stable marginal hard tissue levels in both augmentation groups, 3 years after crown insertion. In addition, the clinical stability of soft and hard tissues was demonstrated in both groups.

Dentigerous Cysts in Children: Clinical, Radiological, and Healing Aspects

Background and Objectives: Dentigerous cysts are one of the most frequent pathologies associated with unerupted or impacted teeth. Such cysts show a male predilection and a preference for the mandibular region. Also, they commonly occur in the second and third decades of life, with only 9% occurring in the first decade. The aim of this work is to apply and study the therapeutic algorithms developed for dentigerous cysts and their outcomes, from the early diagnostic stage to the complete healing phase of pediatric patients diagnosed with this medical condition. Materials and Methods: The study included 19 pediatric patients diagnosed with dentigerous cysts who underwent the enucleation and extraction or conservative attitude of the associated tooth. The bony healing was also followed-up 9 months after the surgery. Results: A higher incidence in the posterior area of the mandible and maxilla was observed, as well as a higher incidence in boys. The 9 months postoperative radiographic assessment showed that the bony defects were completely healed. Conclusions: A thorough understanding of the nature of the lesion backed by a good clinical history and by state-of-the-art radiographic and radiologic examinations can go a long way in helping the surgeon to choose the correct therapeutic approach and to ameliorate the medical condition in the best long-term interest of the young patient. The considered dentigerous cyst cases demonstrated that an early diagnosis and treatment of this pathology is followed by a responsive treatment.

Automatic Segmentation of the Nasolacrimal Canal: Application of the nnU-Net v2 Model in CBCT Imaging

Background/Objectives: There are various challenges in the segmentation of anatomical structures with artificial intelligence due to the different structural features of the relevant region/tissue. The aim of this study was to detect the nasolacrimal canal (NLC) using the nnU-Net v2 convolutional neural network (CNN) model in cone beam-computed tomography (CBCT) images and to evaluate the successful performance of the model in automatic segmentation. Methods: CBCT images of 100 patients were randomly selected from the data archive. The raw data were transferred to the 3D Slicer imaging software in DICOM format (Version 4.10.2; MIT, Massachusetts, USA). NLC was labeled using the polygonal type of manual method. The dataset was split into training, validation and test sets in a ratio of 8:1:1. nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over Union (IoU), F1-Score and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, DC, IoU, F1-Scores and 95% HD metric values were found to be 0.8465, 0.7341, 0.8480 and 0.9460, respectively. According to the data obtained, the receiver-operating characteristic (ROC) curve was drawn and the AUC value under the curve was determined to be 0.96. Conclusions: These results showed that the proposed nnU-Net v2 model achieves NLC segmentation on CBCT images with high precision and accuracy. The automated segmentation of NLC may assist clinicians in determining the surgical technique to be used to remove lesions, especially those affecting the anterior wall of the maxillary sinus.