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.

The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni’s classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject’s gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.

(1) Background: The tongue plays a key role in the stomatognathic system in carrying out oral functions. The aim of this study was to identify the association between tongue pressure and orthodontic parameters. (2) Methods: This study is a cross-sectional multicentered cohort study with IRB approval. During routine orthodontic initial examinations, the following data were recorded: age, sex, angle classification, overjet (OJ), overbite (OB), arch sizes, tongue width, and maximum tongue pressure (MTP). The association between MTP and orthodontic parameters was analyzed using Pearson’s correlation analysis and the Student’s t-test. (3) Results: There is a positive correlation between MTP and age between ages 10 and 20 (R = 0.47, p < 0.01). There is a negative correlation with MTP and age between 20 and 40 (R = −0.30, p < 0.05). There are negative correlations between MTP and OJ (R = −0.278, p < 0.01)) and OB (R = −0.374, p < 0.01). While there is no statistical significance between MTP and tongue width, there is a statistically significant difference between age and tongue width (R = 0.22482, p < 0.05). There is no statistical significance between MTP and sex, angle classification, arch length, intercanine width, and intermolar width. (4) Conclusion: An earlier decline in MTP is observed with patients with malocclusion. This implies that patients with malocclusion should seek early treatment for the malocclusion.

This study aimed to investigate the release of metallic ions from cobalt–chromium (Co-Cr) alloys fabricated by additive manufacturing (AM) for comparison with dental casting. Co-Cr alloys were fabricated via AM using selective laser melting (SLM) and electron beam melting (EBM) in powder-bed fusion. Polished and mechanically ground specimens were prepared. Each specimen was analyzed using an electron probe microanalyzer (EPMA). Each specimen was immersed in an acidic saline solution for 7 days in accordance with ISO 10271: 2020. The EPMA indicated the segregation of some elements in the as-prepared SLM and EBM specimens, whereas the polished and ground specimens exhibited a homogenous elemental distribution. The total amount of ion release from the SLM and EBM specimens was confirmed to be less than 7 μg/cm², which was less than 42 μg/cm² for the cast specimen. The polished and ground specimens exhibited an even lower ion release than the as-prepared specimens. The amount of ions released from the Co-Cr alloy was less than the 200 μg/cm² requirement of ISO 22674: 2022. Co-Cr alloys fabricated by SLM and EBM could provide superior corrosion resistance to cast specimens. AM could be a valuable method for fabricating appliances and denture frameworks in dentistry.

This study aimed to assess the accuracy of a method of predicting post-movement root position during orthodontic treatment using a 3D digital crown/root model (3DCRM) created with pre-movement records of both cone-beam computed tomography (CBCT) and dental arch digital scans. Pre- and post-movement CBCT scans and dental arch digital scans of five patients who had completed orthodontic treatments were used in this study. The 3DCRM was superimposed onto the post-movement scanned dental arch to identify the post-movement root position (test method). Post-movement CBCT (referenced as the current method) served as the control to identify the actual post-movement root position. 3D-coordinate analysis revealed no significant differences between the test and current methods along the X and Y axes. However, the discrepancy on the Z axis (especially in cases of intrusion) was greater than that in all other directions for all three tooth types examined (p < 0.05). A strong positive correlation between the degree of discrepancy and the distance of tooth movement was observed on the Z axis (r = 0.71). The 3DCRM method showed promising potential to accurately predict root position during orthodontic treatments without the need for a second CBCT. However, root resorption, which affected the Z axis prediction, needs to be closely monitored using periapical radiographs to complement this method.

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.