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.

Objectives The aim of this study was to describe the process of regeneration of damaged salivary glands due to ionizing radiations by bone marrow mesenchymal stem cells (BM-MSCs) transplantation that have been given hypoxic preconditioning with 1% O2 concentration. Materials and Methods Stem cell culture was performed under normoxic (O2: 21%) and hypoxic conditions by incubating the cells for 48 hours in a low oxygen tension chamber consisting of 95% N2, 5% CO2, and 1% O2. Thirty male Wistar rats were divided into four groups: two groups of control and two groups of treatment. A single dose of 15 Gy radiation was provided to the ventral region of the neck in all treatment groups, damaging the salivary glands. BM-MSCs transplantation was performed in the treatment groups for normoxia and hypoxia 24-hour postradiation. Statistical Analysis Statistical analysis was done using normality test, followed by MANOVA test (p < 0.05). Results There was a significant difference in the expression of binding SDF1-CXCR4, Bcl-2 (p < 0.05) and also the activity of the enzyme α-amylase in all groups of hypoxia. Conclusion BM-MSCs transplantation with hypoxic precondition increases the expression of binding SDF1-CXCR4, Bcl-2 that contributes to cell migration, cell survival, and cell differentiation.

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.

Abstract

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.

Automatic segmentation of mandibular cortical bone is challenging due to the appearance of teeth that have similar intensity with the bone tissue and the variety of bone intensity. In this paper we propose a new method for automatic segmentation of mandibular cortical bone on cone-beam computed tomography (CBCT) images. The bone tissue is segmented by using Gaussian mixture model for histogram thresholding. The mandibular inferior cortical bone is obtained by incorporating several polynomial models to fit the structure of cortical bone on coronal slices. The buccal and lingual cortical plate is separated by using histogram thresholding for teeth elimination and polynomial fitting for shape extraction. After performing 3D reconstruction, the volumetric cortical bone is obtained. The proposed method gives average accuracy, sensitivity, and specificity value of 96.82%, 85.96%, 97.60%, respectively. This shows that the proposed method is promising for automatic and accurate segmentation of mandibular cortical bone on CBCT images.

Segmentation of three-dimensional (3D) medical images using deep learning is a challenging task due to the lack of a 3D medical image dataset and their ground truth, resource memory limitations, and imbalanced dataset problem. In this paper, we propose advanced deep learning network for segmentation of 3D medical images. The proposed Multi-projection Network can preserve resource memory by applying two-dimensional (2D) kernels while still obtaining the 3D information from the image by incorporating slices from different planar projections of the 3D image to achieve good segmentation results. The proposed network uses a weighted cost function to address the imbalanced dataset problem and introduces an adaptive weight that considers the probability of each class in the image. The experimental results showed that the proposed Multi-projection Network can produce the highest sensitivity (true positive rate) compared to other architectures despite the high class imbalance in the dataset and small amount of training data.

Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.