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Aim: The adjustment and transfer of a stable occlusion can be a major challenge in prosthetic rehabilitations. The aim of this study was to assess a non-invasive treatment option for complex prosthetic rehabilitations and occlusal analyses using 3D-printed restorations clinically. Materials and Methods: Eleven patients received a partial or complete rehabilitation with the aid of 3D-printed restorations (n=171). After 12 months of clinical service, all restorations were analyzed using the United States Public Health Service (USPHS) criteria. Results: The 12-month clinical data revealed that 3D-printed restorations showed a survival rate of 84.4%. Complications occurred mostly regarding the anatomical form (7%) or marginal integrity (6AC%) and were consequently rated “Charlie” or “Delta.” Color stability and color match of 3D-printed restorations were rated “Alpha” in 83% and 73%, respectively, of all restorations. Marginal inflammation was rated “Alpha” in 89% of all restorations. An excellent surface texture and no secondary caries or postoperative sensitivities (100%) were observed. Conclusions: 3D-printed restorations might be an alternative treatment option for initiating complex prosthetic rehabilitations. Technical complications rarely occurred. Biological complications did not occur at all. The color stability showed promising results after 12 months of clinical service. However, the results should be interpreted with caution. Long-term results with a high number of restorations should be awaited. Keywords: 3D-printing, additive manufacturing, CAD/CAM, color stability, in vivo, wear behavior

Aim: To report on a novel digital superimposition workflow that enables measuring the supra-crestal peri-implant soft tissue dimensions all along implant treatment and afterwards. Materials and Methods: A preoperative CBCT and intra-oral scans (IOS) are successively taken before surgery, at the end of the healing period, at prosthesis delivery, and over time; they are digitally superposed on a dedicated software. Then, the stereolithography files (STL) of the healing abutment, of the prosthetic abutment and the crown are successively merged into the superposition set of IOSs. Result: The workflow protocol of merging successively the STL of each item into the superposition set of IOSs enables capturing the dimensions of the height and width of the supra-crestal soft tissues, at every level of the healing abutment, the prosthetic abutment and the crown. In addition, it allows measuring the vertical distance that the crown exerts pressure on the gingiva and the thickness of the papillae at every level of the abutment. Conclusion: This novel digital superimposition workflow provides a straightforward method of measuring the vertical and horizontal dimensions of the supra-crestal peri-implant soft tissues, including the papillae, at each stage of the implant treatment process. It allows investigating a certain number of soft tissue variables that were previously inaccessible to clinical research. It should help enhancing our comprehension of the peri-implant soft tissue dynamics. Keywords: CBCT, clinical research, digital merging, gingival height, gingival width, intra-oral scan, papilla, peri-implant soft tissues

Purpose: A reference method for quantifying contaminations on two-piece abutments manufactured using CAD/CAM has not yet been established. In the present in vitro study, a pixel-­based machine learning (ML) method for detecting contamination on customized two-piece abutments was investigated and embedded in a semiautomated quantification pipeline. Materials and methods: Forty-nine CAD/CAM zirconia abutments were fabricated and bonded to a prefabricated titanium base. All samples were analyzed for contamination by scanning electron microscopy (SEM) imaging followed by pixel­-based ML and thresholding (SW) for contamination detection; quantification was performed in the postprocessing pipeline. Wilcoxon signed-rank test and Bland-Altmann plot were applied to compare both methods. The contaminated area fraction was recorded as a percentage. Results: There was no statistically significant difference between the percentages of contamination areas (median = 0.004) measured with ML (median = 0.008) and with SW (median = 0.012), asymptotic Wilcoxon test: P = 0.22. The Bland-Altmann plot demonstrated a mean difference of -0.006% (95% confidence interval [CI] from -0.011% to 0.0001%) with increased values from a contamination area fraction of > 0.03% for ML. Conclusion: Both segmentation methods showed comparable results in evaluating surface cleanliness; pixel-based ML is a promising assessment tool for detecting external contaminations on zirconia abutments. Further studies are required to investigate the clinical performance of this tool. Keywords: computer-aided design, scanning electron microscopy, machine learning, ultrasonics, hygiene, dental implant abutments