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Li, H., T. Sakai, A. Tanaka, M. Ogura, C. Lee, S. Yamaguchi, and S. Imazato. “Interpretable AI Explores Effective Components of CAD/CAM Resin Composites.” Journal of Dental Research, (April 2022). https://doi.org/10.1177/00220345221089251., High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials’ composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs and explore the components that affect flexural strength as the first step. The composition of 12 commercially available products and flexural strength were collected from the manufacturers and literature. The initial data consisted of 16 attributes and 12 samples. Considering that the input data for each sample were recognized as a multidimensional vector, a fluctuation range of 0.1 was proposed for each vector and the number of samples was augmented to 120. Regression algorithms—that is, random forest (RF), extra trees, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting—were used to develop 5 ML models to predict flexural strength. An exhaustive search and feature importance analysis were conducted to analyze the effective components that affected flexural strength. The R2 values for each model were 0.947, 0.997, 0.998, 0.983, and 0.927, respectively. The relative errors of all the algorithms were within 15%. Among the high predicted flexural strength group in the exhaustive search, urethane dimethacrylate was contained in all compositions. Filler content and triethylene glycol dimethacrylate were the top 2 features predicted by all models in the feature importance analysis. ZrSiO4 was the third important feature for all models, except the RF model. The ML models established in this study successfully predicted the flexural strength of CAD/CAM RCBs and identified the effective components that affected flexural strength based on t

Yamaguchi S., Lee C., Karaer O., et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. Journal of Dental Research, 98(11), 1234-1238. © 2019 Sage Publications. DOI: 10.1177/0022034519867641., 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.

Item does not contain fulltext, This in vivo pilot study was carried out to test the antibacterial effect of glass ionomer containing chlorhexidine (test group) in comparison to conventional glass ionomer (control group). Fifty 6- to 11-year-old children with one occlusal lesion in a molar were randomly allocated to test and control groups in a parallel-group design. The cavity walls and one half of the floor were cleaned and restored with one of the materials without dentine conditioning. The restorations were removed after 7 days. Dentine samples were taken from the cleaned (affected dentine) and noncleaned area (infected dentine) at baseline and at day 7. Samples were anaerobically and aerobically cultivated for mutans streptococci, lactobacilli and total viable bacterial count (TVC) following common laboratory procedures. ANCOVA was used to test for treatment effects. Seven days after treatment, a significant decrease in anaerobic and aerobic bacterial counts (p = 0.0001) was shown. Lower numbers of anaerobic lactobacilli (p = 0.02), TVC (p = 0.008) and aerobic lactobacilli and TVC (p = 0.03), but not of mutans streptococci, were indicated in the test group compared to the control group. A significant reduction in aerobic lactobacilli from infected dentine treated with the glass ionomer containing chlorhexidine (p = 0.05) was observed whereas in affected dentine, anaerobic mutans streptococci, lactobacilli and TVC and aerobic TVC and mutans streptococci were significantly lower in the test group 7 days after treatment (p = 0.01). We conclude that the present pilot study revealed lower microorganism counts in chlorhexidine-containing glass ionomers than in conventional glass ionomers for both affected and infected dentine over a 7-day period.

Contains fulltext : 50662.pdf (publisher's version ) (Closed access), OBJECTIVES: Since atraumatic restorative treatment (ART) involves removal of carious lesions with manual instruments, improvement of filling materials to guarantee greater success should be considered. This study aimed to evaluate antibacterial, physical, and bonding properties of glass-ionomer cements (GIC) containing chlorhexidine (CHX), and to determine optimal concentrations for incorporation of agents to obtain antibacterial GICs for use with the ART approach. METHODS: CHX diacetate combined with CHX dihydrochloride was added to control GIC powder to obtain concentration ratios of 1/0, 2/0, 3/0, 1/1, or 2/2% w/w. Antibacterial activity of each cement against Streptococcus mutans, Lactobacillus casei or Actinomyces naeslundii was examined using agar-diffusion methods, and release of CHX was analyzed by HPLC. Compressive strength, bond strength to dentin, and setting time were measured, and compared with those of control samples. RESULTS: All experimental GICs exhibited inhibition of three bacteria, but sizes of inhibition zones and concentrations of CHX released were not dependent upon CHX content. Incorporation of CHX diacetate at 2% or greater, significantly decreased compressive strength, and bond strength to dentin was adversely affected by addition of CHX diacetate at 2% or more (p<0.05, ANOVA, Fisher's PLSD test), although setting time was extended a little by addition of any concentrations of CHX. SIGNIFICANCE: The present results demonstrate that experimental GICs containing CHX are effective in inhibiting bacteria associated with caries, and incorporation of 1% CHX diacetate is optimal to give appropriate physical and bonding properties.