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. 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. 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 test (α=.05) and compared the different misfit levels using analysis of variance (1-way ANOVA), followed by the Tukey HSD test (α=.05). The highest values for the differences between passive and active fit were found for the V150 and H100 misfit simulations (<.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 (>.05). In the horizontal misfit group of NE-NE abutment configuration, H70 revealed no significant difference from the control group cast (>.05). 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.

中文翻译：

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对具有不同接合和非接合钛基体组合的种植体支撑假体进行失配模拟：第 3 部分：射线照相评估

种植体支持的假体的配合对其机械和生物稳定性起着重要作用。临床上，通常通过射线照相评估假肢的贴合度，但这种方法依赖于操作者的主观评估。可用的数字工具是否可以优化假肢贴合的评估尚不确定。这项体外研究的目的是评估图像处理程序对植入物支持的假体主动和被动配合差异的放射线检测的影响。通过模拟 3 种不同种植体基台配置的垂直和水平错位，对两种植体支撑的螺钉固位修复体进行了分析。使用 2 个内连接钛种植体制作了 7 个铸件：1 个为对照；1 个为对照。3 垂直 (V) 失配为 50 µm、100 µm、150 µm；3 个水平 (H) 失配分别为 35 µm、70 µm、100 µm。制作了三十个条形氧化锆框架，并根据其与 2 个接合（EE）、2 个非接合（NE-NE）以及接合和非接合（E-NE）钛基体的附着情况分为 3 组（n=10）。对每个模型在被动和主动贴合情况下（1 螺钉测试）对每个样本进行数字化平行根尖周 X 线照片，EE 样本除外，因为 EE 样本仅固定在对照、H35 和 H70 模型上，因为与模型上的贴合情况剩下的演员阵容很差。使用 ImageJ 软件程序测量第二个植入物（B 侧）上选定的感兴趣区域的平均灰度值 (MGV)。使用检验 (α=.05) 测试被动和主动条件之间 MGV 测量值的差异，并使用方差分析（单向方差分析）比较不同的失配水平，然后进行 Tukey HSD 检验 (α=.05) ）。在 V150 和 H100 失配模拟中发现了被动和主动配合之间差异的最高值 (<.05)。MGV 之间存在统计差异，但有一些例外：最小的模拟失配（H35 和 V50）显示出与最大模拟失配（V150、H100）以及存在参与组件的组中的 H70 之间存在统计显着的 MGV 差异（> .05）。在 NE-NE 基台配置的水平错位组中，H70 与对照组石膏没有显着差异 (>.05)。测量被动贴合和主动贴合之间的 MGV 差异可能是一种很有前途的替代方案，用于检测因不贴合而导致的种植体-基台连接中 70 至 150 µm 的间隙。然而，该程序不足以检测 <50 µm 的间隙，不能统一应用于所有类型的种植体基台连接，并且需要 2 次 X 射线照射。