[Four-year follow-up study of onlay and occlusal veneer restorations on posterior teeth].

OBJECTIVE: To investigate the survival rate and clinical failure reasons of onlay and occlusal veneer restorations retrospectively, and to put forward valuable suggestions for the selection of clinical indications. METHODS: A total of 102 patients and 124 teeth treated by one of the authors from 2016 to 2019 were subjected to CAD/CAM lithium silicate reinforced glass-ceramic onlay or veneer restorations of premolars and molars, including 43 teeth with pulp vitality, 81 endodontic treated teeth, and occlusal thickness of restoration was 1.5 mm. After four years of restoration, retrospective surveys were conducted to record the survival rate of restorations, the causes of restoration failure, and patient satisfaction rates, and the survival rate of restorations between vital teeth and endodontic treated teeth and among restored teeth was statistically analyzed by Chi-square test. RESULTS: The survival rates of restorations on vital teeth and endodontic treated teeth were 95.5% and 90.0%, respectively, the average survival rate was 90.2%. The survival rates of vital teeth were higher than those of endodontic treated teeth without statistical difference. There was also no statistically significant difference among the tooth locations. The causes of failure included the cracking of the restoration, the loss of the restoration, the fracture of the abutment teeth, secondary caries below the adjacent contact point, and food impaction caused by the loosening of the adjacent contact point. The overall patient satisfaction rate was 91.5%. CONCLUSION: The 4-year survival rate of glass-ceramic onlays and occlusal veneers is lower than that of the full crown restoration, and there are more complications than that of the single-crown restorations. The design of the restoration should be carefully selected based on the vitality of the abutment tooth and the remaining amount of tooth tissue. When there is too little tooth structure left, a post and crown should be selected for restoration. Adequate strength and thickness of the restoration should be ensured to prevent food impaction. Due to the small amount of abutment tooth preparation, it has the advantages of less stimulation of the pulp and periodontal tissue, and can be recommended as a trial restoration.

Deep learning-based tooth segmentation methods in medical imaging: A review.

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Quantum Neural Network for Quantum Neural Computing.

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.

[Influence of autoclave sterilization on dimensional stability and detail reproduction of 5 additional silicone impression materials].

OBJECTIVE: To evaluate the dimensional stability and detail reproduction of five additional silicone impression materials after autoclave sterilization. METHODS: Impressions were made on the ISO 4823 standard mold containing several marking lines, in five kinds of additional silicone. All the impressions were sterilized by high temperature and pressure (135 °C, 212.8 kPa) for 25 min. Linear measurements of pre-sterilization and post-sterilization were made with a measuring microscope. Statistical analysis utilized single-factor analysis with pair-wise comparison of mean values when appropriate. Hypothesis testing was conducted at alpha = 0.05. RESULTS: No significant difference was found between the pre-sterilization and post-sterilization conditions for all locations, and all the absolute valuse of linear rate of change less than 8%. All the sterilization by the autoclave did not affect the surfuce detail reproduction of the 5 impression materials. CONCLUSIONS: The dimensional stability and detail reproduction of the five additional silicone impression materials in the study was unaffected by autoclave sterilization.

[In vivo model to evaluate the accuracy of complete-tooth spectrophotometer for dental clinics].

OBJECTIVE: To test ΔE between measured value and right value from the Crystaleye complete-tooth spectrophotometer, and to evaluate the accuracy rate of the spectrophotometer. METHODS: Twenty prosthodontists participated in the study. Each of them used Vita 3D-Master shadeguide to do the shade matching, and used Crystaleye complete-tooth spectrophotometer (before and after the test training) tested the middle of eight fixed tabs from shadeguide in the dark box. The results of shade matching and spectrophotometer were recorded. The accuracy rate of shade matching and the spectrophotometer before and after training were calculated. RESULTS: The average accuracy rate of shade matching was 49%. The average accuracy rate of the spectrophotometer before and after training was 83% and 99%. CONCLUSIONS: The accuracy of the spectrophotometer was significant higher than that in shade matching, and training can improve the accuracy rate.

[The influence of toothguide training box on shade matching veracity].

PURPOSE: The aim of the study was to evaluate the influence of toothguide training box(TTB) on shade matching veracity, and compare the differences of influence on shade matching veracity of tabs of shadeguide and on that of tabs out of shadeguide. METHODS: 31 graduated students who had 1 to 5-year clinical experience without color blindness were trained by TTB once a week for 3 weeks. Elapsed time and scores from the training system were recorded each time. And everyone was tested by 29 tabs of Vita 3D-Master shadeguide and 7 tabs out of Vita 3D-Master shadeguide before the first training as the base line and tested again after the last training as the test after training. SPSS 10.0 software package was used for analysis. The variances of scores and elapsed time were analyzed by ANOVA, and the differences of veracity between tabs of shadeguide and that of tabs out of shadeguide by Wilcoxon signed ranks test. RESULTS: The scores of the tests were rising via training. There was significant difference (P<0.01) between the first (900.29+/-51.68) and the third training (933.81+/-32.94). The elapsed time was turning shorter via training. There is significant difference (P<0.01) between the first [(46.29+/-13.29)min] and the second training [(32.68+/-8.81)min], and also between the first and the third training (30.00+/-7.07)min. The veracity of test after training was higher than the veracity of base line. A significant difference (P<0.01) of shade matching veracity was demonstrated between base line and test after training in both tabs of shadeguide (60.37%+/-18.33%,46.08%+/-22.04%) and the total (37.10%+/-11.58%,28.34%+/-11.73%), but that difference in tabs out of shadeguide (13.82+/-14.98%,10.60%+/-11.65%) was not significant(P>0.01). CONCLUSION: TTB can improve the shade matching ability of prosthodontists, but has limited influence on the shade matching ability of tabs out of shadeguide.

[Comparison of shade matching accuracy of tabs of shadeguide and tabs out of shadeguide].

OBJECTIVE: To compare the shade matching accuracy between tabs of shade guide and tabs out of shade guide by prosthodontists. METHODS: Sixty-two graduate students who had 1 to 5 years clinical experience without color blindness were included in this study. Twenty-nine tabs from Vita 3D-Master shadeguide and 7 tabs out of Vita 3D-Master shade guide but from Vita Bleachedguide 3D-Master were taken as the targets of shade matching and observed under the standard light. Subjects' shade matching accuracy on tabs of shadeguide and tabs out of shadeguide was recorded. Frequency of right match was analyzed using Monte Carlo exact probability-test. Wilcoxin test was used to analyze the difference between each group on ranked data of value, chroma and hue. RESULTS: The shade matching accuracy of tabs of shade guide [45.9% (199/434)] was significantly higher than that of tabs out of shade guide [9.0% (39/434)]. There was significant difference between the two kinds of tabs of ranked data of value [0.2 (0.1 - 0.3), 0.4 (0.4 - 0.5)] and chroma [0.2 (0.1 - 0.4), 0.5 (0.4 - 0.6), P < 0.01, but there was no significant difference in hue data [0.3 (0.1 - 0.4), 0.3 (0.1 - 0.4)]. CONCLUSIONS: The shade matching ability of prosthodontists on tabs out of shade guide is lower than that on tabs of shadeguide. To fulfill clinical need, more training is necessary.

[Application and comparison of two shade guide training systems].

OBJECTIVES: To evaluate the effect of shade guide training box and shade guide training software on shade matching ability of observers when used separately. To find out the difference between two training plans when the two training methods were used in combination, and to provide information on shade matching training system. METHODS: Sixty-two postgraduate dental students who specialized in prosthodontics with 1 to 5 year clinical experience were enrolled in this study. At base line, each participant were asked to match 7 standard shade tabs which have been randomly chosen from Vita 3D-Master shade guide and 7 intermediate shade tabs from Vita bleached guide 3D-Master. Then the subjects were allocated to 2 groups [Toothguide Training Box (TTB) group and Toothguide Training (TT) group] according to the baseline data. Participants in group TTB received training session once a week for 3 weeks, while those in group TT received TT training session once a week for 3 weeks. All participants took a middle term shade-matching test. Then the two groups exchange the training methods and repeat the whole process, a final test was given to each participant. The elapsed time and number of accurate shade matching were recorded for each training session. Wilcoxon signed ranks test and ANOVA were used in data analysis. RESULTS: There were no significant differences in the number of accurate shade matching (standard shade tab and the sum) between group TTB (4.4 +/- 1.3 and 5.3 +/- 1.6) and TT (4.0 +/- 1.4 and 4.9 +/- 1.5) in the middle term test with higher value found in group TTB. In the final test, the number of accurate shade matching (standard shade tab and the sum) in group TT (4.9 +/- 0.8 and 6.4 +/- 0.8) was higher than that in group TTB (4.7 +/- 1.1 and 5.8 +/- 0.9), but significant difference was found only when the sum number of accurate shade matching was compared between the two groups (P < 0.05). There was no significant difference between data from middle term test and from final test in group TTB; while in group TT, the number of accurate shade matching in the final test was significantly increased compared with that in the middle term test (P < 0.05). CONCLUSIONS: When used in combination, TT training followed by TTB training is recommended.