Background: Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.Methods: The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden’s index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).Results: Sensitivity, specificity, and Youden’s index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976–0.983, impacted teeth), 0.975 (95%CI: 0.972–0.978, full crowns), and 0.935 (95%CI: 0.929–0.940, residual roots), 0.939 (95%CI: 0.934–0.944, missing teeth), and 0.772 (95%CI: 0.764–0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).Conclusions: The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3–10 years of experience. However, the AI framework for caries diagnosis should be improved.

Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software. A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05). The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05). AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.

Background: Dental caries prevalence reaches the first peak around 6 years old. It is necessary to take effective measures to prevent and treat caries at this stage. This study investigated the prevalence and associated factors of dental caries among schoolchildren aged 6–8 years in Hangzhou City, China. Methods: A cross-sectional survey was conducted in Hangzhou from October 2017 to May 2018. Oral health status of schoolchildren in 1st and 2nd grades of primary schools aged 6–8 years was examined by well-trained examiners according to the WHO criteria. Questionnaires about potential caries-related factors were distributed to their parents. ANOVA test and logistic regression were conducted for the statistical analyses (α = 0.05). Results: A total of 7959 pairs of schoolchildren and their parents were invited to participate, and 5595 were included in this study according to the eligible criteria (response rate: 70.3%). The overall prevalence and mean dmft/DMFT of dental caries in the investigated schoolchildren were 52.78% and 2.11. The prevalence and mean dmft/DMFT were 39.05% and 1.63 in the deciduous teeth, while these were 21.45% and 0.48 in the permanent teeth, respectively. For the first permanent molars (FPMs), the rate of fully eruption and pit and fissure sealing (PFS) were 79.09% and 6.60%, respectively. Multiple logistic regression showed that girl (OR = 1.12, 95% CI 1.01–1.25, p < 0.05), seldom or never brush teeth (OR = 2.36, 95% CI 1.08–5.44, p < 0.05), consuming sweet food or drink once or more time a day (OR = 1.14, 95% CI 1.00–1.29, p < 0.05; OR = 1.21, 95% CI 1.07–1.36, p < 0.05), dental visit experiences (OR = 1.58, 95% CI 1.35–1.86, p < 0.001) were positively and no tooth pain in the past 12 months (OR = 0.55, 95% CI 0.38–0.80, p < 0.05) were negatively associated with caries. Conclusions: Dental caries was prevalent among schoolchildren aged 6–8 years in Hangzhou, and was associated with gender, frequency of toothbrushing, sweet food or drink consuming, tooth pain and dental visit experiences. A large number of fully erupted FPMs did not receive timely PFS.

Background: Self-glazed zirconia (SZ) restorations are made by a novel additive three-dimensional gel deposition approach, which are suitable for a straightforward completely digital workflow. SZ has recently been used as minimally invasive veneer, but its clinical outcomes have not been clarified yet. This study aimed to evaluate the preliminary clinical outcomes of SZ veneers compared with the widely used lithium disilicate glass–ceramic veneers made by either pressing (PG) or milling (MG) process. Methods: Fifty-six patients treated with SZ, PG, and MG veneers by 2 specialists between June 2018 and October 2022 were identified. Patients were recalled for follow-up at least 1 year after restoration. Clinical outcomes were assessed by 2 independent evaluators according to the modified United States Public Health Service (USPHS) criteria. Overall patient satisfaction was assessed using visual analogue scale (VAS), and analyzed by one-way ANOVA. Chi-square test was applied to compare the difference in the success and survival rates among the 3 groups. Results: A total of 51 patients restored with 45 SZ, 40 PG, and 41 MG veneers completed the study, with a patient dropout rate of 8.9%. Mean and standard deviation of follow-up period was 35.0 ± 14.7 months. All restorations performed well at baseline, except for 2 SZ veneers with mismatched color (rated Bravo). During follow-up, marginal discrepancy (rated Bravo) was found in 4 MG veneers and 1 PG veneer, and partially fractured (rated Charlie) was found in another 2 PG veneers. The survival rate of SZ, PG, and MG veneers was 100%, 95%, and 100%, with a success rate of 95.56%, 92.50%, and 90.24%, respectively, none of which were significantly different (p = 0.099 and 0.628, respectively). The mean VAS score of SZ, PG, and MG was 95.00 ± 1.57, 93.93 ± 2.40, and 94.89 ± 2.00 respectively, without significant difference (p > 0.05). Conclusion: SZ veneers exhibited comparable preliminary clinical outcomes to PG and MG veneers, which could be considered as a feasible option for minimally invasive restorative treatment.