De novo bone formation around implants with a surface based on a monolayer of multi-phosphonate molecules. An experimental in vivo investigation.

OBJECTIVES: The purpose of this experimental in vivo investigation was to evaluate the influence of modifying the implant surface by adding a monolayer of multi-phosphonate molecules on the de novo bone formation and osseointegration. MATERIAL AND METHODS: The study was designed as an animal preclinical trial with intra-animal control and two healing periods, 2 and 8 weeks, to compare implants with an identical macro-design but with two different surfaces. Eight female Beagle dogs participated in the study. Control implants had a moderately rough surface combining sandblasting and acid etching; test implants had an additional monophosphonate layer covalently bonded to titanium. Histologic and radiographic (micro-CT) outcome variables were evaluated. RESULTS: The first bone-to-implant contact (fBIC) was located more coronally for the test implants at the first (0.065 mm (95% CI = -0.82, 0.60)) and second healing milestones (0.17 mm (95% CI = -0.9, 0.55)). Most coronal BIC of the test implants displayed a higher percentage of osseointegration, +6.33% and +13.38% after 2 and 8 weeks, respectively; however, the differences were not statistically significant. The micro-CT examination did not show any BIC difference. CONCLUSIONS: The monophosphonate layer coating demonstrated clinical, histological, and radiographic results similar to the control surface.

Hard and soft tissue changes after guided bone regeneration using two different barrier membranes: an experimental in vivo investigation.

OBJECTIVE: To assess the contour and volumetric changes of hard and soft tissues after guided bone regeneration (GBR) using two types of barrier membranes together with a xenogeneic bone substitute in dehiscence-type defects around dental implants. MATERIAL AND METHODS: In 8 Beagle dogs, after tooth extraction, two-wall chronified bone defects were developed. Then, implants were placed with a buccal dehiscence defect that was treated with GBR using randomly: (i) deproteinized bovine bone mineral (DBBM) covered by a synthetic polylactic membrane (test group), (ii) DBBM plus a porcine natural collagen membrane (positive control) and (iii) defect only covered by the synthetic membrane (negative control group). Outcomes were evaluated at 4 and 12 weeks. Micro-CT was used to evaluate the hard tissue volumetric changes and STL files from digitized cast models were used to measure the soft tissues contour linear changes. RESULTS: Test and positive control groups were superior in terms of volume gain and contour changes when compared with the negative control. Soft tissue changes showed at 4 weeks statistically significant superiority for test and positive control groups compared with negative control. After 12 weeks, the results were superior for test and positive control groups but not statistically significant, although, with a lesser magnitude, the negative control group exhibited gains in both, soft and hard tissues. CONCLUSIONS: Both types of membranes (collagen and synthetic) attained similar outcomes, in terms of hard tissue volume gain and soft tissue contours when used in combination with DBBM CLINICAL RELEVANCE: Synthetic membranes were a valid alternative to the "gold standard" natural collagen membrane for treating dehiscence-type defects around dental implants when used with a xenogeneic bone substitute scaffold.

Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs.

The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student's diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.