Effect of Silver Nanoparticles, Zinc Oxide Nanoparticles and Titanium Dioxide Nanoparticles on Microshear Bond Strength to Enamel and Dentin.

AIM: This study was aimed to evaluate whether antibacterial pretreatment of enamel and dentin with silver nanoparticles (SNPs), zinc oxide nanoparticles (ZNPs) and titanium dioxide nanoparticles (TNPs) has any effect on the microshear bond strength of an etch-and-rinse adhesive system. MATERIALS AND METHODS: Eighty human third molars were randomly assigned to eight subgroups (n = 10). Enamel groups included no pretreatment (E), pretreatments with SNPs (ESNP), ZNPs (EZNP) and TNPs (ETNP) before acid etching and adhesive application. Dentinal groups included no pretreat-ment (D), pretreatments with SNPs (DSNP), ZNPs (DZNP) and TNPs (DTNP). The specimens were bonded by Adper Single Bond and polyvinyl chloride microtubes and were restored with Z250 composite. The bonded surfaces underwent microshear bond strength (uSBS) test. Data in megapascal (MPa) were analyzed with the Kruskal-Wallis test and the Mann-Whitney test (p = 0.05). RESULTS: There was not a significant difference among the groups in enamel (p > 0.05). There was no significant difference between the application of three nanoparticles and the control group in dentin. However, DSNPs had a higher uSBS (25.60 ± 14.61) than that of the DZNPs and DTNPs groups (p = 0.03 and p = 0.001, respectively). Also, the mean uSBS value was lower in dentin groups compared to the respective enamel groups (p < 0.05) except for groups DSNPs and ESNPs in which no significant difference was found (p > 0.05). CONCLUSION: Pretreatment with SNPs, TNPs, and ZNPs can be suggested to achieve potent antibacterial activities without compromising the bond strength. The best result was obtained for pretreatment with SNPs compared to pretreatment with TNPs or ZNPs in dentin and enamel, albeit the differences were not significant in the enamel groups. CLINICAL SIGNIFICANCE: Effective antibacterial treatment prior to adhesive bonding application is desirable to provide successful restoration if it would not adversely affect the bond strength of the adhesive system. Nanoparticles can be applied to meet this goal.

Uncertainty-based Active Learning by Bayesian U-Net for Multi-label Cone-beam CT Segmentation.

INTRODUCTION: Training of Artificial Intelligence (AI) for biomedical image analysis depends on large annotated datasets. This study assessed the efficacy of Active Learning (AL) strategies training AI models for accurate multilabel segmentation and detection of periapical lesions in cone-beam CTs (CBCTs) using a limited dataset. METHODS: Limited field-of-view CBCT volumes (n = 20) were segmented by clinicians (clinician segmentation [CS]) and Bayesian U-Net-based AL strategies. Two AL functions, Bayesian Active Learning by Disagreement [BALD] and Max_Entropy [ME], were used for multilabel segmentation ("Lesion"-"Tooth Structure"-"Bone"-"Restorative Materials"-"Background"), and compared to a non-AL benchmark Bayesian U-Net function. The training-to-testing set ratio was 4:1. Comparisons between the AL and Bayesian U-Net functions versus CS were made by evaluating the segmentation accuracy with the Dice indices and lesion detection accuracy. The Kruskal-Wallis test was used to assess statistically significant differences. RESULTS: The final training set contained 26 images. After 8 AL iterations, lesion detection sensitivity was 84.0% for BALD, 76.0% for ME, and 32.0% for Bayesian U-Net, which was significantly different (P < .0001; H = 16.989). The mean Dice index for all labels was 0.680 ± 0.155 for Bayesian U-Net and 0.703 ± 0.166 for ME after eight AL iterations, compared to 0.601 ± 0.267 for Bayesian U-Net over the mean of all iterations. The Dice index for "Lesion" was 0.504 for BALD and 0.501 for ME after 8 AL iterations, and at a maximum 0.288 for Bayesian U-Net. CONCLUSIONS: Both AL strategies based on uncertainty quantification from Bayesian U-Net BALD, and ME, provided improved segmentation and lesion detection accuracy for CBCTs. AL may contribute to reducing extensive labeling needs for training AI algorithms for biomedical image analysis in dentistry.