Multi-scale analysis of the influence of filler shapes on the mechanical performance of resin composites using high resolution nano-CT images

Objective: The aim of this study was to investigate the criteria for predicting the fracture initiation of resin composites (RCs) at the micro-scale and assess the influence of filler shapes on the flexural properties of RCs by combining nano-CT imaging and in silico multi-scale analysis. Methods: Experimental RCs composed of irregular-shaped (IS) silica filler (31.2 vol%/50.0 wt%) and Bis-GMA/TEGDMA were prepared. The RC specimens were scanned by a nano-CT with 500-nm resolution, and 10 micro-scale models (100 × 100 × 100 μm) were randomly extracted from a scanned region. In silico micro-scale models containing sphere-shaped (SS) fillers with the same volume content as the experimental RC were designed. Each RC model's elastic modulus and Poisson's ratio at the macro-scale were calculated using homogenization analysis. The flexural strength of the RC models were predicted by finite element analysis using the elastic moduli and Poisson's ratio values. Results: Significantly greater elastic modulus values were obtained in the X, Y, and Z directions for RC models containing IS fillers than SS fillers. Similarly, smaller Poisson's ratio values were observed in the Y and Z directions for RC model containing IS fillers than SS fillers (p < 0.05). The flexural strength of RC model containing IS fillers was significantly greater than the RC model containing SS fillers (p < 0.05). Significance: The in silico multi-scale analysis established in this study demonstrated that RC model containing irregular-shaped fillers had greater flexural strength than RC model loaded with SS fillers, suggesting that the mechanical strength of the RC can be improved by optimizing the shape of the silica fillers.Sakai T., Li H., Abe T., et al. Multi-scale analysis of the influence of filler shapes on the mechanical performance of resin composites using high resolution nano-CT images. Dental Materials 37, 168 (2021); https://doi.org/10.1016/j.dental.2020.10.030

Color matching ability of resin composites incorporating supra-nano spherical filler producing structural color

Objective: The aim of this study was to evaluate the optical properties of supra-nano spherical fillers with different diameters and the color matching ability of resin composites (RC) incorporating these fillers. Methods: Two types of SiO2–ZrO2 nano fillers with different diameters (150 nm and 260 nm) were used. The size distribution of each filler was measured and filler morphology was observed. The colors and spectral reflection spectra were measured by a spectral reflectometer. Experimental RCs incorporating ϕ150-nm/ϕ260-nm filler (D150RC/D260RC) were prepared. For the base dentin part, disc specimens (Estelite Astelia: A1B, A2B, A3B, A3.5B, or A4B) were prepared with a cylindrical cavity. Estelite Astelia with NE shade was layered on top as the enamel layer. Disk specimens with different cavity depths were prepared using A3B shade. Experimental RC was used to fill the cavity, and spectral reflection spectrums were obtained and analyzed. Filtek Supreme Ultra (FSU) with A3B shade was used (n = 10) as a control. Results: Both ϕ150-nm and ϕ260-nm nano fillers showed uniform spherical shape and exhibited no aggregation. The maximum peaks of the spectral reflection spectra of the ϕ150-nm and ϕ260-nm nano fillers were 380 nm and 580 nm, producing structural colors close to blue and yellow, respectively. The spectral reflection spectrum of FSU had a broad peak at 540 nm, and D150RC had a significant peak at 420 nm. The D260RC specimen had a broad peak at 680 nm. The peaks of D150RC and D260RC significantly decreased in accordance with the shift in base RC shade from A1B to A4B. There was no significant difference in the peak of the reflection spectral spectra among different cavity depths of D260RC. These results suggest that the experimental RC could reflect base RC colors via the matrix resin, and the amount of transmitted light from the base RC was not much different with cavity depth. Significance: D260RC producing structural color demonstrated a broad spectrum and reduction in brightness and chromatic value by adapting to surrounding restorative materials, suggesting its ability to enhance the chameleon (blending) effects to improve color matching. D260RC showed better color matching ability than resin composite containing uniformly sized ϕ150-nm SiO2–ZrO2 supra-nano spherical filler.Yamaguchi S., Karaer O., Lee C., et al. Color matching ability of resin composites incorporating supra-nano spherical filler producing structural color. Dental Materials 37, e269 (2021); https://doi.org/10.1016/j.dental.2021.01.023

Multi-scale analysis of the effect of nano-filler particle diameter on the physical properties of CAD/CAM composite resin blocks

The objective of this study was to assess the effect of silica nano-filler particle diameters in a computer-aided design/manufacturing (CAD/CAM) composite resin (CR) block on physical properties at the multi-scale in silico. CAD/CAM CR blocks were modeled, consisting of silica nano-filler particles (20, 40, 60, 80, and 100 nm) and matrix (Bis-GMA/TEGDMA), with filler volume contents of 55.161%. Calculation of Young’s moduli and Poisson’s ratios for the block at macro-scale were analyzed by homogenization. Macro-scale CAD/CAM CR blocks (3 × 3 × 3 mm) were modeled and compressive strengths were defined when the fracture loads exceeded 6075 N. MPS values of the nano-scale models were compared by localization analysis. As the filler size decreased, Young’s moduli and compressive strength increased, while Poisson’s ratios and MPS decreased. All parameters were significantly correlated with the diameters of the filler particles (Pearson’s correlation test, r = −0.949, 0.943, −0.951, 0.976, p < 0.05). The in silico multi-scale model established in this study demonstrates that the Young’s moduli, Poisson’s ratios, and compressive strengths of CAD/CAM CR blocks can be enhanced by loading silica nanofiller particles of smaller diameter. CAD/CAM CR blocks by using smaller silica nano-filler particles have a potential to increase fracture resistance.This is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering on 19 May 2017, available at https://doi.org/10.1080/10255842.2017.1293664

Development of artificial intelligence model for supporting implant drilling protocol decision making

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. Conclusions: 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.Sakai T., Li H., Shimada T., et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. Journal of Prosthodontic Research 67, 360 (2023); https://doi.org/10.2186/jpr.JPR_D_22_00053