Although pre-treatment parameters for subtractive computer-aided design/computer-aided manufacturing (CAD/CAM) materials have been thoroughly investigated, data regarding additive restorative materials designed for permanent use in the oral cavity are scarce. This study investigated the effects of abrasive materials and blasting pressure on the bond strength of 3D-printed resins used in permanent restorations. A total of n = 44 test specimens additively manufactured from Formlabs' `Permanent Crown Resin' were prepared. Three groups with n = 11 specimens were airborne-particle abraded with 50 µm aluminium oxide at 1 bar, 2 bar or 3 bar. The control group did not receive any further airborne-particle abrasion after post-processing with 50 µm glass beads. Cylindrical superstructures were bonded to the prepared surfaces using RelyX Unicem 2. The bonded specimens were then subjected to artificial ageing via thermocycling. Subsequently, shear bond strength (sbs) tests were conducted. Data were analysed with the Kruskal-Wallis test (α = 0.05). After post-processing with glass beads, average shear bond strength values of 10.13 ± 7.62 MPa were achieved. When using aluminium oxide and 1 bar blasting pressure, significantly higher average sbs values of 25.57 ± 7.04 MPa were revealed (p = 0.009). The bond strength increased with higher blasting pressures (28.14 ± 6.35 MPa at 2 bar (p = 0.005); 30.15 ± 6.46 MPa at 3 bar (p < 0.001)). However, the shear tests revealed increased failure within the specimen base at higher blasting pressures. Bond strength significantly improved when airborne-particle abrasion with aluminium oxide was applied. However, increased airborne-particle abrasion pressure led to more failures at the base plate level of the specimens. Aluminium oxide increases the adhesive strength of 3D-printed dental restorative materials. Although the adhesive bond increased slightly with increasing airborne-particle abrasion pressure, the 3D restorative materials also exhibited an increased failure rate within the restorative material in the shear bond strength test with increasing blasting pressure.

Objectives: This study aimed to assess the impact of a deep learning model on oral radiologists’ ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty. Methods: This study used an annotated dataset and a beta version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for the presence/absence of apical radiolucencies. Four oral radiologists participated in a cross-over reading scenario, analysing the radiographs under 2 conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using Alternative Free-Response Receiver Operating Characteristic - Area Under the Curve (AFROC-AUC), sensitivity, specificity, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance. Results: No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (β = 12, 95% CI, 11-13, P < 0.001), while radiologists’ professional status was positively associated with diagnostic accuracy (β = 0.02, 95% CI, 0.00-0.04, P = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists’ experience in diagnostic accuracy. Conclusion: AI did not significantly enhance radiologists’ overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.

Ensuring a secure bond between a framework structure and layering composite resin veneer is essential for a long-lasting dental restoration. A variety of primer systems are available to facilitate the adhesive bonding. Nevertheless, the growing preference for efficiency and simplicity in dentistry has made the one-bottle universal primers a desirable option. This study aims to compare the effectiveness of universal primers on the shear bond strength (SBS) of base metal alloy (BMA) and zirconia to layering composite resin. Each 160 BMA and zirconia 20 × 10 × 5 mm test specimen was fabricated. Eight different primers (SunCera Metal Primer, Metal Primer Z, Reliance Metal Primer, Alloy Primer, MKZ Primer, Monobond Plus, ArtPrime Plus, and Clearfil Ceramic Primer Plus) were applied to 20 specimens in each group. Subsequently, a 5 × 2 mm composite resin build-up was applied. SBS tests were performed after 24 h of water storage and after thermocycling (25,000 cycles, 5–55 °C). On BMA, after water storage for 24 h, the bond strength values ranged from 26.53 ± 3.28 MPa (Metal Primer Z) to 29.72 ± 2.00 MPa (MKZ Primer), while after thermocycling, bond strength values ranged from 25.19 ± 1.73 MPa (MKZ Primer) to 27.69 ± 2.37 MPa (Clearfil Ceramic Primer Plus). On a zirconia base, after 24 h, the bond strengths values ranged from 22.63 ± 2.28 MPa (Reliance Primer) to 29.96 ± 2.37 MPa (MKZ Primer) and from 23.77 ± 3.86 MPa (Metal Primer Z) to 28.88 ± 3.09 MPa (Monobond Plus) after thermocycling. While no significant difference in bond strength was found between the primers on the BMA base, five primer combinations differed significantly from each other on zirconia (p = 0.002–0.043). All primers achieved a bond strength greater than 23 MPa on both framework materials after thermocycling. Thus, all primers tested can be applied to both framework materials with comparable results.