Purpose: To review the current clinical studies regarding the accuracy of implant computer-guided surgery in partially edentulous patients and investigate potential influencing factors. Study selection: Electronic searches on the PubMed and Cochrane Central Register of Controlled Trials databases, and subsequent manual searches were performed. Two reviewers selected the studies following our inclusion and exclusion criteria. Qualitative review and meta-analysis of the implant placement accuracy were performed to analyze potential influencing factors. Angular deviation, coronal deviation, apical deviation, and depth deviation were evaluated as the accuracy outcomes. Results: Eighteen studies were included in this systematic review, including six randomized controlled trials, nine prospective studies, and three retrospective clinical studies. A total of 1317 implants placed in 642 partially edentulous patients were reviewed. Eight studies were evaluated using meta-analysis. Fully guided surgery showed statistically higher accuracy in angular (P <0.001), coronal (P <0.001), and apical deviation (P <0.05) compared with pilot-drill guided surgery. A statistically significant difference (P <0.001) was also observed in coronal deviation between the bounded edentulous (BES) and distal extension spaces (DES). A significantly lower angular deviation (P <0.001) was found in implants placed using computer-aided design/computer-aided manufacturing (CAD/CAM) compared to the conventional surgical guides. Conclusions: The edentulous space type, surgical guide manufacturing procedure, and guided surgery protocol can influence the accuracy of computer-guided surgery in partially edentulous patients. Higher accuracy was found when the implants were placed in BES, with CAD/CAM manufactured surgical guides, using a fully guided surgery protocol.
In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.
Background The impact of the jaw bone condition, such as bone quantity and quality in the implant placement site, affecting the accuracy of implant placement with computer-guided surgery (CGS) remains unclear. Therefore, this study aimed to evaluate the influence of bone condition, i.e., bone density, bone width, and cortical bone thickness at the crestal bone on the accuracy of implant placement with CGS. Methods A total of 47 tissue-level implants from 25 patients placed in the posterior mandibular area were studied. Implant placement position was planned on the simulation software, Simplant® Pro 16, by superimposing preoperative computed tomography images with stereolithography data of diagnostic wax-up on the dental cast. Implant placement surgery was performed using the surgical guide plate to reflect the planned implant position. The post-surgical dental cast was scanned to determine the position of the placed implant. Linear and vertical deviations between planned and placed implants were calculated. Deviations at both platform and apical of the implant were measured in the bucco-lingual and mesio-distal directions. Intra- and inter-observer variabilities were calculated to ensure measurement reliability. Multiple linear regression analysis was employed to investigate the effect of the bone condition, such as density, width, and cortical bone thickness at the implant site area, on the accuracy of implant placement (α = 0.05). Result Intra- and inter-observer variabilities of these measurements showed excellent agreement (intra class correlation coefficient ± 0.90). Bone condition significantly influenced the accuracy of implant placement using CGS (p < 0.05). Both bone density and width were found to be significant predictors. Conclusions Low bone density and/or narrow bucco-lingual width near the alveolar bone crest in the implant placement site might be a risk factor influencing the accuracy of implant placement with CGS.
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