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.

SummaryThe purpose of this review was to assess the available literature regarding bonding between current adhesive systems and computer-aided design/computer-aided manufacturing (CAD/CAM) indirect resin materials, to provide clinicians with a comparative overview of the relevant bonding procedures. An electronic search was performed through PubMed based on the keywords CAD/CAM and dental bonding. Additional relevant literature was obtained from the citations in the articles. A total of 313 papers were identified, of which 281 were excluded as being unsuitable, and an additional 3 papers were identified, giving a total of 32 articles that are included in this review. Based on this survey, it is recommended that microretentive surfaces should be generated by either blasting or hydrofluoric acid etching. This initial process should be followed by silanization to ensure chemical adhesion prior to bonding to CAD/CAM indirect resin composite materials (including Lava Ultimet, KATANA AVENCIA block, Gradia Block, Cerasmart, Paradigm, and Block HC) and CAD/CAM polymer-infiltrated ceramics (such as Vita Enamic). The use of materials containing methyl methacrylate (MMA) also appears to improve the bonding of CAD/CAM poly(methyl methacrylate) (PMMA) resin materials (including XHIPC-CAD/CAM, artBloc Temp, and Telio).

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The effect of cleaner containing 10-methacryloyloxydecyl dihydrogen phosphate (MDP) for removing temporary cement remnants on dentin surface was evaluated. Flat dentin surfaces were wet-polished (Co) and HY-BOND temporary cement hard (Shofu) was applied to the surface. This temporary cement was removed using an air-scaler (Sc), brush (Br), or phosphoric acid and NaOCl (NC). A prototype cleaner containing MDP (Kuraray Noritake Dental, Tokyo, Japan) was used with agitation mode (MC+AG). KATANA Avencia block (Kuraray Noritake Dental) was luted with SA Cement Plus Automix (Kuraray Noritake Dental). Co showed significantly higher bond strength than Sc or Br (p<0.001 each). Bond strengths with NC (p=0.99) and MC+AG (p=0.38) did not differ significantly from that with Co. Transmission electron microscopy revealed sufficient interaction of MC+AG. Cleaner containing MDP can effectively remove temporary cement by agitation, and can be expected to improve the chemical bonding ability by binding more MDP to dentin.

The purpose of this review was to assess the literature regarding four types of fixed dental prostheses (FDPs)/resin-bonded FDPs (RBFDPs) to provide clinicians with a comparative overview of two myths: “RBFDPs are easy to debond in patients’ mouths” and “cantilever RBFDPs still have some clinical problems, especially in terms of overloading the abutment teeth and being easy to debond”. A total of 782 papers were identified, 753 of which were judged unsuitable and thus excluded, leaving a total of 29 articles for inclusion in this review. The results indicated that 1) Two-retainer RBFDPs achieve clinical results comparable to full-coverage three-unit FDPs; 2) Cantilever RBFDPs show excellent long-term clinical outcomes (especially in incisor teeth) compared with other FDPs; 3) RBFDPs typically show less catastrophic failure than conventional FDPs, rebonding should be considered when debonding occurs; and 4) Cantilever RBFDPs can be recommended as defect replacement prostheses for maxillary lateral incisors and mandibular incisor teeth. Scientific field: Prosthodontics, Adhesive dentistry, Esthetic dentistry