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Lower success rates of dental implants placed adjacent to teeth with periapical lesions or to endodontically treated teeth were reported; however, the results were inconsistent.

There is some evidence to support an association between the endodontic condition of the adjacent tooth and the success of dental implants, but it is not enough to support a causative relationship. Nevertheless, clinicians should treat any active sources of infection and inflammation in adjacent teeth prior to insertion of dental implants.

There is some evidence to support an association between the endodontic condition of the adjacent tooth and the success of dental implants, but it is not enough to support a causative relationship. Nevertheless, clinicians should treat any active sources of infection and inflammation in adjacent teeth prior to insertion of dental implants.

The aim of this study was to evaluate the effect of ultraviolet (UV) photofunctionalization on peri-implant osteogenesis of miniscrews.

Titanium orthodontic miniscrews were placed in the maxillary premolar-molar region of 17 patients undergoing fixed orthodontic treatment. This was a split-mouth study wherein the miniscrews on one side were treated with UV photofunctionalization and those on the other side were left untreated. Photofunctionalization was performed by placing the miniscrews in a chamber consisting of UV-A and UV-C lights for 15 minutes immediately prior to implantation. Efficacy of the UV chamber was assessed by examining stereomicroscopic images of a 10-μL droplet of double-distilled water placed on a UV-treated titanium pellet. Retrieved miniscrews were evaluated for bone-miniscrew contact (BMSC) using scanning electron microscopy (SEM) based on a custom-devised 4-point objective scoring system. Surface element deposition of miniscrews was estimated using energy-dispersive x-ray spectrometry (EDX). Ratios of Ca/Ti and Ca/P were calculated for upper, middle, and lower regions of all miniscrews.

Increased spread of the water droplet over the UV-treated pellet showed that photofunctionalization converted the titanium surface from hydrophobic to superhydrophilic. SEM imaging revealed that BMSC was greater in the photofunctionalized group, but only in the lower third of miniscrews, and this was not statistically significant. EDX analysis revealed that Ca/Ti and Ca/P ratios in both groups were similar. Thus, there was no significant difference between peri-implant osteogenesis of UV-treated and untreated miniscrews.

These results suggest that UV photofunctionalization did not enhance the biologic potential of titanium orthodontic miniscrews in clinical application.

These results suggest that UV photofunctionalization did not enhance the biologic potential of titanium orthodontic miniscrews in clinical application.

Intraoral bone blocks from the external oblique are the gold standard for alveolar ridge bone grafting, but the limited amount of available bone limits their use for larger defects. The objective of this study was to compare whether different graft designs of intraoral bone blocks could affect the amount of bone gain.

In this in vitro study, 20 pig jaws were used to harvest bone blocks and subsequently augment single-wall bone defects. AT7867 chemical structure Each bone graft was first used as a full block, and then the same block was divided lengthwise into two blocks, with one block fixed at a distance as a cortical shell and the second block particulated to fill the gap between graft and bone. Three stereolithographic (STL) files (pre-OP, full block, split block) were generated using an intraoral scanner. All STL files were evaluated for volume gain and horizontal bone dimensions.

A mean volume gain of 0.36 cm

(SD 0.09) was achieved for the full block and 0.78 cm

(SD 0.14) for the split block using the same block. The difference was statistically significant (P < .0001). A mean horizontal bone gain of 4.37 mm (SD 0.93) was achieved with a full block and 5.77 mm (SD 0.85) with the shell technique (P < .0001).

With the same amount of bone removed, first as a full block and then as a split block, the split-block technique achieved a significantly higher bone gain compared with the full-block design.

With the same amount of bone removed, first as a full block and then as a split block, the split-block technique achieved a significantly higher bone gain compared with the full-block design.

The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental implant from a radiograph.

A data augmentation procedure provided a total of 1,206 dental implant radiographic images of three different brands for six models (Nobel Biocare NobelActive [NNA] and Br.nemark System [NBS], Straumann Bone Level [SBL] and Tissue Level [STL], and Zimmer Biomet Dental Tapered Screw-Vent [ZTSV] and SwissPlus [ZSP]). They were divided into a test group (n = 241; 19.9%) and a training and validation group (n = 965; 80%). Preprocessing and transfer learning were applied to a pretrained GoogLeNet Inception CNN network. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) of the CNN model were evaluated.

The diagnostic accuracy was 93.8% (95% CI 87.2% to 99.4%), the sensitivity was 93.5% (95% CI 84.2% to 99.3%), the specificity was 94.2% (95% CI 83.5% to 99.4%), the positive predictive value was 92% (95% CI 83.9% to 97.2%), and the negative predictive value was 91.5% (95% CI 80.2% to 97.1%). The deep CNN algorithm achieved an AUC of 0.918 (95% CI 0.826 to 0.973) on NNA, 0.922 (95% CI 0.831 to 0.964) on NBS, 0.909 (95% CI 0.844 to 0.982) on SBL, 0.890 (95% CI 0.783 to 0.945) on STL, 0.931 (95% CI 0.867 to 0.979) on ZTSV, and 0.911 (95% CI 0.811 to 0.957) on ZSP.

The deep CNN model had a very good performance in identifying a dental implant from a radiograph. A huge and varied database of radiographs would have to be built up to be able to identify any dental implant.

The deep CNN model had a very good performance in identifying a dental implant from a radiograph. A huge and varied database of radiographs would have to be built up to be able to identify any dental implant.