[Comparison of the registration methods for the three-dimensional facial scans applied to the design of full-arch implant supported restoration].

OBJECTIVE: To compare the registration accuracy of three-dimensional (3D) facial scans for the design of full-arch implant supported restoration by five methods and to explore the suitable registration method. METHODS: According to the criteria, ten patients with maxillary edentulous jaw or end-stage dentition requiring implant supported restorations were enrolled in this study. A special rim with individual feature marks reflected appropriate occlusal relationship and esthetic characteristics was made for each patient. Both 3D facial scan data of natural laughter and with opener traction to expose the teeth or occlusal rim of each patient were acquired by facial scan and input to the digital analysis software Geomagic Qualify 2012. The dataset was superimposed by five different methods: seven facial anatomical landmark points alignment, facial immobile area alignment (forehead and nasal area), facial anatomical landmark points and immobile area combining alignment, facial feature points alignment, facial and intraoral feature points alignment with the same local coordinate system. The three-dimensional deviation of the same selected area was calculated, the smaller the deviation, the higher the registration accuracy. The 3D deviation was compared among the three registration methods of facial anatomical landmark points, facial immobile area alignment and the combination of the above two methods. Friedman test was performed to analyze the difference among the three methods (α=0.05). The effect of the aid of the facial and intraoral feature points were evaluated. Paired t test were performed to analyze the difference (P<0.05). RESULTS: The average three-dimensional deviation of the selected area after alignment with the facial anatomical landmarks was (1.501 2±0.406 1) mm, significantly larger than that of the facial immobile area best-fit alignment [(0.629 1±0.150 6) mm] and the combination of the two methods[(0.629 1±0.150 6) mm] (P < 0.001). The aid of the facial feature points could significantly reduce the deviation (t=1.001 3, P < 0.001). There was no significant statistical difference in the remaining groups. CONCLUSION: The forehead area of the 3D facial scan can be exposed as much as possible. The establishment of facial characteristic landmark points and the use of the invariant area alignment can improve the accuracy of registration. It should be clinically feasible to apply three-dimensional facial scan to the design of full-arch implant supported restoration with the registration of the immobile area on the face especially the forehead area.

[Three-year clinical effects of a modified semi-fixed bridge on restoring a missing posterior tooth].

OBJECTIVE: To observe the effects of a modified semi-fixed bridge to restore a missing posterior tooth for three years. METHODS: In the study, 38 cases with a posterior tooth missed were chosen as experiment group. The inclusion criteria for these cases required that one abutment tooth be defected, and another abutment intact. The missed posterior tooth was restored with a modified semi-fixed bridge in which a metal wing retainer was applied to the intact abutment tooth and a full crown retainer was applied to the defected abutment tooth, and the non-rigid connector linked to the metal wing retainer and the pontic, while the rigid connector linked to the full crown retainer and the pontic. And 42 cases with same abutment conditions were chosen as control group. These control patients were treated with classical rigidly-fixed partial dentures. The clinical effects regarding the bridge survival, abutment conditions, and etc. were observed by examination. RESULTS: The average observation time for experiment group was 40.3 months (from 9 to 60 months). One case failed with loose crown retainer after 7 months' usage. Another failed case was the same performance after 20 months' wearing. The restorations were rebounded after the inner face of crowns was treated, and they were used normally. No loose abutment and retainer, no decayed abutment teeth were found, and the marginal adaptations between retainers and abutment teeth were good. The average observation time of control group was 44.7 months(from 22 to 50 months). No secondary decays and loose abutment teeth were detected and the marginal adaptation of the retainers was good. CONCLUSION: This modified semi-fixed bridge has similar survival rate and clinical effects to restore a missing posterior tooth when compared with the traditional rigidly-fixed partial dentures. But the long-term observation is required.

Effect of odanacatib on root resorption and alveolar bone metabolism during orthodontic tooth movement.

The aim of this study was to investigate the effect of local administration of odanacatib (ODN) on orthodontic root resorption and the status of alveolar bone metabolism in rat molars. All specimens were scanned using microcomputed tomography and then the raw images were reconstructed. The total volume of the root resorption craters of the 60 g-NS (normal saline) group was higher than in the 60 g-ODN group and the control group. In the 60 g-NS group, the bone volume fraction values of alveolar bone were significantly decreased compared with the other 2 groups. There were no significant differences in the bone volume fraction values of the tibiae among the 3 groups. The results of tartrate-resistant acid phosphatase-positive (TRAP+) numbers showed that there was no difference between the 60 g-NS group and the 60 g-ODN group. The expression of cathepsin K was decreased significantly in the 60 g-ODN group. These results indicate that ODN reduces orthodontics-induced external root resorption and increases alveolar bone metabolism. This may be because ODN inhibits the activity of odontoclasts, but maintains the quantity of odontoclasts and enhances bone formation. ODN promotes local alveolar bone metabolism, but does not affect systemic bone metabolism.

[Application of machine learning in orthodontics].

With the increasing integration of artificial intelligence (AI) with other fields, machine learning, one of the major methods in AI, has been extensively applied in dentistry in the stage of diagnosis, therapy and prognosis evaluation. Nowadays, numerous studies have reported the applications of machine learning in orthodontics in two major aspects, including image identification and clinical decision support system. Herein, we reviewed the application progress of machine learning in orthodontics in order to provide new insight for the future precision medicine.