The effect of different implant neck configurations on soft and hard tissue healing: a randomized-controlled clinical trial

Objective: To compare the soft and hard tissue healing and remodeling around tissue-level implants with different neck configurations after at least 1 year of functional loading. Material and methods: Eighteen patients with multiple missing teeth in the posterior area received two implants inserted in the same sextant. One test (T) implant with a 1.8 mm turned neck and one control (C) implant with a 2.8 mm turned neck were randomly assigned. All implants were placed transmucosally to the same sink depth of approximately 1.8 mm. Peri-apical radiographs were obtained using the paralleling technique and digitized. Two investigators blinded to the implant type-evaluated soft and hard tissue conditions at baseline, 6 months and 1 year after loading. Results: The mean crestal bone levels and soft tissue parameters were not significantly different between T and C implants at all time points. However, T implants displayed significantly less crestal bone loss than C implants after 1 year. Moreover, a frequency analysis revealed a higher percentage (50%) of T implants with crestal bone levels 1–2 mm below the implant shoulder compared with C implants (5.6%) 1 year after loading. Conclusion: Implants with a reduced height turned neck of 1.8 mm may, indeed, lower the crestal bone resorption and hence, may maintain higher crestal bone levels than do implants with a 2.8 mm turned neck, when sunk to the same depth. Moreover, several factors other than the vertical positioning of the moderately rough SLA surface may influence crestal bone levels after 1 year of function.

Assessment of Potential Orthodontic Mini-implant Insertion Sites Based on Anatomical Hard Tissue Parameters: A Systematic Review

To estimate the applicability of potential sites for insertion of orthodontic mini-implants (OMIs) by a systematic review of studies that used computed tomography (CT) or cone beam CT to evaluate anatomical bone quality and quantity parameters, such as bone thickness, available space, and bone density.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.