The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

This study aimed to use quantitative and qualitative evaluations based on micro-tensile bond strength (μTBS) to clarify the appropriate immediate dentin sealing (IDS) approach for improving the bonding of CAD/CAM ceramic crown restorations. Forty-eight extracted human molars were prepared to obtain standardized abutment specimens and divided into three groups: no IDS (group C: control), IDS performed by a single application of an all-in-one adhesive system (group A), and IDS performed by the combined application of an adhesive system and a flowable resin composite (group F). All specimens were restored with a ceramic crown fabricated by a chair-side CAD/CAM system and were divided into no-stress and stressed groups. After cyclic loading (78.5 N; total, 3 × 105 cycles; 90 cycles/min) on the specimens in the stressed group, all specimens were sectioned. The μTBS values for the occlusal and mesioaxial walls were measured (n = 16) and analyzed statistically. The quantitative bonding performance of groups A and F were superior to that of group C, regardless of the cyclic loading and abutment wall conditions. Group F showed the maximum bond strength and the highest bond durability in the qualitative bonding performance even under the cyclic loading condition simulating clinical mastication.

The effect of differences in the type of restoration and adhesive resin cement system on the bonding of CAD/CAM ceramic restoration after cyclic loading was examined quantitatively and qualitatively. Seventy-two human maxillary first molars were divided into three restoration groups: MOD-inlay, MODP-onlay, and crown. Immediate dentin sealing was applied to the exposed dentin of all prepared specimens. The 24 specimens of each restoration group were further divided into another three groups, and a different adhesive resin cement system was applied to each group for cementation. All restoratives were fabricated from feldspathic-ceramic-blocks and cemented with each adhesive resin cement system according to the manufacturer’s instructions. The microtensile bond-strength was measured after cyclic loading and was not significantly affected by differences in the type of restoration or adhesive resin cement system. However, the type of restorations and adhesive resin cement systems did show significant differences in terms of the bonding reliability.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies

Incremental techniques are always required for clinical cases of deep and/or large cavities restored with resin composite materials. The purpose of this study was to examine the bonding states of class 2 direct resin composite restoration applied by various incremental techniques after cyclic loading to simulate the intra-oral environment to define the appropriate technique. Three types of resin composites, namely, bulk-fill (B), flowable (F), and conventional resin composite (C), were applied to standardized class 2 cavities by incremental techniques with single- or bi-resin restoratives. After cyclic loading, the micro-tensile bond strength (μ-TBS) of the dentin cavity floor was measured. The Weibull modulus and Weibull stress values at 10%/90% probability of failure were analyzed. Single-resin incremental restorations with B or F and bi-resin incremental restorations with F + B and F + C demonstrated superior μ-TBS (quantitative ability), bonding reliability, and durability (qualitative ability) compared with the single-resin restoration with C (as control). Furthermore, F + B and F + C restoration yielded an excellent performance compared with the single-resin restorations with B, F, and C. In particular, the F + C restoration, which indicates not only the maximum mean µ-TBS, but also the highest values of the Weibull parameters, may be the optimal restoration method, including the esthetic benefits.

The purpose of this study is to investigate barriers experienced by dental students when choosing professional careers. An anonymous online survey with eight questions extracted from the annual American Dental Education Association Survey for U.S. dental school seniors (UDS) and two Japan-specific questions was given to Japanese dental school seniors (JDS) in 2019, 2020, and 2021. The results were compared to UDS data. This study was approved by the Institutional Review Board of Harvard Medical School and included 1053 respondents. The parental education and income levels of JDS were higher than UDS, and educational debt of JDS was lower than UDS. The top reason for choosing dentistry for JDS was influence and expectation from family members, while the top reason for UDS was service to others. Although both groups showed interest in teaching in academia, the majority chose to enter private practice immediately post-graduation, and would consider teaching during their mid or late career. Barriers for dental students staying in academia varied between the two countries. For JDS, the main barrier was an anxiety around stable income. For UDS, it was their educational debt. Dental educators and policymakers need to consider these barriers to improve the sustainability of dental students choosing an academic career path.