This review paper describes several recent modification methods for biocompatible titanium dental implant surfaces. The micro-roughened surfaces reviewed in the literature are sandblasted, large-grit, acid-etched, and anodically oxidized. These globally-used surfaces have been clinically investigated, showing survival rates higher than 95%. In the past, dental clinicians believed that eukaryotic cells for osteogenesis did not recognize the changes of the nanostructures of dental implant surfaces. However, research findings have recently shown that osteogenic cells respond to chemical and morphological changes at a nanoscale on the surfaces, including titanium dioxide nanotube arrangements, functional peptide coatings, fluoride treatments, calcium–phosphorus applications, and ultraviolet photofunctionalization. Some of the nano-level modifications have not yet been clinically evaluated. However, these modified dental implant surfaces at the nanoscale have shown excellent in vitro and in vivo results, and thus promising potential future clinical use.

Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

Kim HK, et al. J Adv Prosthodont. 2016 Feb;8(1):37-42. http://dx.doi.org/10.4047/jap.2016.8.1.37