To develop a risk of bias tool for pre-clinical dental materials research studies that aims to support reporting of future investigations and improve assessment in systematic reviews. A four-stage process following EQUATOR network recommendations was followed, which included project launch, literature review, Delphi process and the tool finalization. With the support of the European Federation of Conservative Dentistry (EFCD) and the Dental Materials Group of the International Association for Dental Research (DMG-IADR), a total of 26 expert stakeholders were included in the development and Delphi vote of the initial proposal. The proposal was built using data gathered from the literature review stage. During this stage, recent systematic reviews featuring dental materials research, and risk of bias tools found in the literature were comprehensively scanned for bias sources. The experts thus reached a consensus for the items, domains and judgement related to the tool, allowing a detailed guide for each item and corresponding signalling questions. The tool features nine items in total, spread between 4 domains, pertaining to the following types of bias: bias related to planning and allocation (D1), specimen preparation (D2), outcome assessment (D3) and data treatment and outcome reporting (D4). RoBDEMAT, as presented, features signalling questions and a guide that can be used for RoB judgement. Its use as a checklist is preferred over a final summary score. RoBDEMAT is the first risk of bias tool for pre-clinical dental materials research, supported and developed by a broad group of expert stakeholders in the field, validating its future use. This new tool will contribute the study field by improving the scientific quality and rigour of dental materials research studies and their systematic reviews. Such studies are the foundation and support of future clinical research and evidence-based decisions.

A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.

Surface Pre-Reacted Glass-ionomer (S-PRG) filler, which releases strontium (Sr²⁺), borate (BO₃³⁻), fluoride (F⁻), sodium (Na⁺), silicate (SiO₃²⁻), and aluminum (Al³⁺) ions at high concentrations, is a unique glass filler that are utilized in dentistry. Because of its multiple-ion releasing characteristics, S-PRG filler exhibits several bioactivities such as tooth strengthening, acid neutralization, promotion of mineralization, inhibition of bacteria and fungi, inhibition of matrix metalloproteinases, and enhancement of cell activity. Therefore, S-PRG filler per se and S-PRG filler-containing materials have the potential to be beneficial for various dental treatments and care. Those include restorative treatment, caries prevention/management, vital pulp therapy, endodontic treatment, prevention/treatment of periodontal disease, prevention of denture stomatitis, and perforation repair/root end filling. This review summarizes bioactive functions exhibited by S-PRG filler and its possible contribution to oral health.

Purpose: This study aimed to develop an artificial intelligence (AI) model to support the determination of an appropriate implant drilling protocol using cone-beam computed tomography (CBCT) images.Methods: Anonymized CBCT images were obtained from 60 patients. For each case, after implant placement, images of the bone regions at the implant site were extracted from 20 slices of CBCT images. Based on the actual drilling protocol, the images were classified into three categories: protocols A, B, and C. A total of 1,200 images were divided into training and validation datasets (n = 960, 80%) and a test dataset (n = 240, 20%). Another 240 images (80 images for each type) were extracted from the 60 cases as test data. An AI model based on LeNet-5 was developed using these data sets. The accuracy, sensitivity, precision, F-value, area under the curve (AUC) value, and receiver operating curve were calculated.Results: The accuracy of the trained model is 93.8%. The sensitivity results for drilling protocols A, B, and C were 97.5%, 95.0%, and 85.0%, respectively, while those for protocols A, B, and C were 86.7%, 92.7%, and 100%, respectively, and the F values for protocols A, B, and C were 91.8%, 93.8%, and 91.9%, respectively. The AUC values for protocols A, B, and C are 98.6%, 98.6%, and 99.4%, respectively.Conclusion: The AI model established in this study was effective in predicting drilling protocols from CBCT images before surgery, suggesting the possibility of developing a decision-making support system to promote primary stability.