Objective:To establish and to evaluate a computer-aided system based on deep-learning for detection and diagnosis of dental approximal caries on periapical radiographs.Methods:One hundred and sixty human premolars and molars extracted for orthodontic or periodontal reasons were obtained from Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Fujian Medical University. A total of 160 periapical radiographic images were divided into a training dataset ( n=80) and a test dataset ( n=80). A deep-learning based computer-aided caries diagnosis system was established and trained. The performances of computer-aided diagnosis system and human observer were compared using receiver operating characteristic (ROC) curves, precision-recall (P-R) curves, the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The AUC values of human observers and caries diagnosis system was compared by using an online statistical tool (SPSSAU 20.0). Chi-square test was used to analyze the differences between human observers and caries diagnosis system (ɑ=0.05). Results:The AUC values of human observers and caries diagnosis system were 0.729 (95% CI: 0.650-0.808) and 0.762 (95% CI: 0.685-0.839), respectively ( P>0.05). No significant differences were found for the specificity, PPV and NPV between the caries diagnosis system and human observers ( P all>0.05). The caries diagnosis system was significantly more sensitive in detecting dental proximal caries than human observers ( P<0.05). For the diagnosis of level-1 caries (caries limited to outer 1/2 of enamel), the sensitivity of human observers and computer-aided detection system were 27% and 77%, respectively ( P<0.05). Conclusions:The computer-aided diagnosis system provided similar accuracy as human observers and significantly better sensitivity than human observers, especially for shallow caries in enamel.

Objective:To develop and evaluate metal artifact removal systems (MARSs) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0 mm, 1.5 mm, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARSs. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.

Objective: To evaluate the effects of quercetin on dentin resistance to erosion and provide evidence-based recommendations for the prevention and therapy of dental erosion. Methods: One hundred and twenty-eight dentin samples were prepared from 50 extracted human wisdom teeth (collected from Department of Oral Surgery, School and Hospital of Stomatology). Ninety-six samples were randomly divided into 8 groups using the following different soaking solutions: deionized water, ethanol (control groups), 12.300 mg/L sodium fluoride, 0.120 mg/L chlorhexidine, 0.183 mg/L epigallocatechin gallate (EGCG), and 0.075, 0.150 and 0.300 mg/L quercetin. In each group, twelve specimen was prepared. Before daily acid challenge, the samples were immersed in the respective solutions for 2 min, rinsed with deionized water, and immersed in artificial saliva for 2 h. The samples were then subjected to 4 cycles of in vitro acid challenges. This protocol was applied for 7 d. The surface microhardness (SMH) and surface profiles were measured before and after erosion using the surface microhardness tester and contact profilometry, respectively. The change in surface profiles and reduction in SMH were used to calculate the substance loss and reduction percentage of SMH (SMH%) respectively. Scanning electron microscope (SEM) images were taken to observe the surface morphology of the samples. Additionally, another thirty two samples were divided into 8 groups (n=4) as mentioned above. The specimens were treated with 10% phosphoric acid and desiccated, immersed in the respective solutions for 2 min, rinsed, and immersed in the artificial saliva at 37 ℃ for 7 d. The content of cross-linked carboxyterminal telopeptide of type Ⅰ collagen (ICTP) in the soaking solutions were measure quantitatively. Results: Compared with the control groups, the application of chlorhexidine, quercetin, and EGCG were effective in preventing the surface softening and substance loss of human dentin after erosion. More specifically, the specimens treated with 0.300 mg/L quercetin exhibited the lowest SMH% [(8.75±4.95)%], the lowest surface substance loss [(2.26±1.16) μm], and the lowest contents of ICTP in the soaking solution [(5.72±0.88) ng], showing significant differences to the chlorhexidine and EGCG treated samples (P<0.05). No significant differences were found in the substance loss and ICTP contents in the three soaking solutions with different concentrations of quercetin(P>0.05). However, the specimens treated with 0.300 mg/L quercetin exhibited significantly lower SMH% than those treated with the other two concentrations of quercetin (P<0.05). Conclusions Within the limitations of the current study, immersion in the quercetin solution is effective in improving the dentin resistance to erosion by inhibiting the dentinal MMP. Among all the concentrations tested, 0.300 mg/L quercetin showed the best performance.

Objective:To develop and evaluate metal artifact removal systems (MARS) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0, 1.5, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARS. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.