ABSTRACT
STATEMENT OF PROBLEM: The fit of implant-supported prostheses plays an important role in their mechanical and biological stability. Clinically, the prosthetic fit is typically assessed radiographically, but this method relies on the operator's subjective evaluation. Whether available digital tools could optimize the evaluation of the prosthetic fit is uncertain. PURPOSE: The purpose of this in vitro study was to evaluate the influence of an image processing program on the radiographic detection of discrepancies in the active and passive fit of implant-supported prostheses. Two-implant-supported screw-retained prostheses were analyzed by simulating the vertical and horizontal misfits of 3 different implant abutment configurations. MATERIAL AND METHODS: Seven casts were fabricated using 2 internal-connection titanium implants: 1 control; 3 with vertical (V) misfit of 50 µm, 100 µm, 150 µm; and 3 with horizontal (H) misfit of 35 µm, 70 µm, 100 µm. Thirty bar-shaped zirconia frameworks were fabricated and divided into 3 groups (n=10) according to their attachment to 2 engaging (E-E), 2 nonengaging (NE-NE), and engaging and nonengaging (E-NE) titanium bases. Digital parallel periapical radiographs were made of each specimen in the passive and active fit situation on each cast (1-screw test), except for the E-E specimens, which were only seated on the control, H35, and H70 casts because the fit on the remaining casts was poor. The mean gray value (MGV) was measured at the chosen regions of interest on the second implant (side B) using the ImageJ software program. Differences in the MGV measurements between the passive and active conditions were tested using a t test (α=.05) and compared the different misfit levels using analysis of variance (1-way ANOVA), followed by the Tukey HSD test (α=.05). RESULTS: The highest values for the differences between passive and active fit were found for the V150 and H100 misfit simulations (P<.05). Statistical differences between the MGVs were found with some exceptions: the smallest simulated misfits (H35 and V50) revealed statistically significant MGV differences from the highest simulated misfits (V150, H100) and from the H70 in the groups where an engaging component was present (P>.05). In the horizontal misfit group of NE-NE abutment configuration, H70 revealed no significant difference from the control group cast (P>.05). CONCLUSIONS: Measuring MGV differences between passive and active fit could be a promising alternative for detecting 70- to 150-µm gaps in the implant-abutment connection that result from the misfit. However, the procedure was not adequate for detecting <50 µm gaps, cannot be uniformly applied to all types of implant-abutment connections, and requires 2 exposures to X-radiation.
ABSTRACT
STATEMENT OF PROBLEM: Prosthesis fit is 1 of the main factors influencing the success and survival of an implant-supported screw-retained restoration. However, scientific validation of the performance of engaging and nonengaging components in a fixed partial denture (FPD) and the effect of their combinations on the fit of FPDs is lacking. The screw resistance test has been used for the fit assessment of screw-retained FPDs. However, objective assessments by using analog and digital devices are now available. PURPOSE: The purpose of this in vitro study was to investigate the effect of engaging and nonengaging components on the fit of screw-retained frameworks, supported by 2 conical connection implants with simulated vertical and horizontal misfits, by performing 2 different screw resistance tests (analog and digital). MATERIAL AND METHODS: Thirty 2-implant-supported bar-shaped zirconia frameworks cemented on two 2-mm titanium bases were fabricated and divided into 3 groups (n=10) according to different abutment combinations: both engaging, engaging and nonengaging, both nonengaging. The fit of each framework was tested on the control cast and on 6 definitive casts simulating 50-, 100-, and 150-µm vertical and 35-, 70-, and 100-µm horizontal misfit levels. The abutment screws were tightened on each implant, and the screw rotation angle was measured both digitally, with a custom-made digital torque wrench and a computer software program, and conventionally, with an analog torque wrench and protractor. Clearly ill-fitting specimens were excluded. The data were statistically analyzed by 1-way analysis of variance (ANOVA) and the Tukey post hoc test (α=.05). RESULTS: Both engaging specimens on the 100-µm horizontal misfit group and on all vertical misfit groups were clearly ill-fitting and excluded. Statistically significant differences among groups with different combinations of abutments were found (P<.05). The engaging abutments had a higher angle of rotation than the nonengaging abutments on all casts. In the horizontal misfit group, both engaging specimens had the highest angle of rotation, followed by engaging and nonengaging and both engaging specimens. In the vertical misfit group, the engaging and nonengaging specimens had the highest angle of rotation on the side of the engaging abutment. The angle of rotation increased with the increasing level of misfit. CONCLUSIONS: Both nonengaging frameworks showed superiority in misfit tolerance, as the angle of rotation was lower than that of the engaging and nonengaging and both engaging frameworks. Conventional and digital torque wrenches showed similar results.