Effect of Combined Application of Hydrofluoric and Phosphoric Acids and Active Irrigation with a Microbrush on Shear Bond Strength of Lithium Disilicate Ceramics to Enamel.

Background: This study assessed the effect of combined application of hydrofluoric (HF) acid and phosphoric acid (PA) and active irrigation (AI) with a microbrush on shear bond strength (SBS) of lithium disilicate (LDS) ceramics to enamel. Materials and Methods: This in vitro study was conducted on 40 extracted teeth that received enamel preparation with a #12 cylindrical bur. Forty IPS e.max LT rods (3mm diameter, 6mm height) were fabricated and randomly assigned to four groups (n = 10) for surface treatment with 5% HF (group 1), 5% HF and AI with a microbrush for 20 seconds (group 2), 5% HF and 32% PA (group 3), and 5% HF and 32% PA plus AI with a microbrush for 20 seconds (group 4). Silane and Choice 2 cement were used for bonding rods to enamel. The SBS was measured by a universal testing machine. Data were analyzed by two-way analysis of variance (ANOVA), Bonferroni, and Chi-square tests (alpha = 0.05). Results: Group 4 had the highest SBS, and group 1 had the lowest SBS (P < 0.05). Group 2 had a significantly higher SBS than group 1, and group 4 had a significantly higher SBS than group 3. AI with a microbrush significantly increased the SBS (P < 0.05), but the application of PA caused no significant change in SBS (P > 0.05). The interaction effect of PA and AI on SBS was not significant (P > 0.05). Conclusion: The application of PA in addition to 5% HF acid caused no significant change in the SBS of LDS ceramic to enamel. However, AI with a microbrush significantly increased the SBS.

Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review.

Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.