Influence of Chairside Simulated Adjustment (Finishing and Polishing) Protocol and Chlorhexidine Mouthwash Immersion on Color Stability and Translucency of 2 and 3 Preshaded Multilayered Monolithic Zirconia

BACKGROUND Preshaded monolithic zirconia (MLZ) is reported to have high translucency. This study aimed to assess the effect of chlorhexidine gluconate (ChG) mouthwash on color and translucency parameter (TP) of 2 different preshaded MLZ dental ceramics after clinical adjustment. MATERIAL AND METHODS Two MLZ disk-shaped specimens [NPM (Nacera Pearl Multi-Shade) (n=72) and CZM (Ceramill Zolid FX Multilayer)] (n=72) were simulated for clinical adjustment, finished, and polished using 2 adjustment kits [recommended kit, third-party kit: Diasynt Plus and SUN (n=12 each)] and later immersed in ChG mouthwash (Avohex) for 2 weeks. Difference in color (ΔE) and TP (Y) were calculated using the CIELab formula after measuring the coordinates (Lab) with a colorimeter. Individual changes in color and TP were assessed on the Clinical acceptance (perceptible) threshold (CAT/CPT) and Translucency perception threshold (TPT), respectively. Differences between the 2 ceramics were assessed using one-way ANOVA and post hoc tests, with all differences considered significant at P<0.05. RESULTS NPM and CZM differed in color at baseline despite having the same Vita shade combination. Between the 2 preshaded MLZ ceramics, NPM showed significant changes in color when adjusted with a third-party kit. Chlorhexidine produced changes in color and TP that were designated as clinically perceptible (ΔE=1.0 to 3.3) on the CAT/CPT and TPT scales, irrespective of the adjustment kit used. ChG produced the least or no changes in glazed MLZ specimens. CONCLUSIONS ChG mouthwash, whenever prescribed for preshaded MLZ restoration, should be adjusted prior to final glazing to avoid clinical adjustments that adversely affects color and translucency of the restoration.

Evaluation of the Effects of Digital Manufacturing, Preparation Taper, Cement Type, and Aging on the Color Stability of Anterior Provisional Crowns Using Colorimetry

BACKGROUND Replicating the 3-dimensional (3D) color of natural teeth in artificial substitutes is challenging. Fixed dental prosthodontics require aesthetic, color-stable provisional restorations. Recent milling and 3D printing digital manufacturing techniques offer improved outcomes. This study assesses color stability in various digital manufacturing methods, tapers, and aging effects on anterior provisional resin restorations. MATERIAL AND METHODS Two all-ceramic tooth preparations on typodont teeth with 10° and 20° tapers were converted into experimental dies. Forty temporary crowns were manufactured using 3D printing and computer-aided design/computer-aided manufacturing (milling). Within these 2 groups, 10 crowns were cemented using a regular and clear provisional cement. All samples were thermocycled to simulate clinical use of 6 months. Color difference formula (CIEDE2000) indicated changes between before and after cementation (ΔE00) and between after cementation and after thermocycling (ΔE00[II]). The color change was considered significant in terms of clinically perceptible (ΔE00 ≤0.62) and acceptable (ΔE00 ≤2.62) changes. One-way ANOVA (P value of less than 0.05) calculated overall differences, which were established using a Fisher post hoc test. RESULTS Crowns cemented with clear cement showed fewer color changes irrespective of the manufacturing technique or taper. Notably, only the 10° 3D-printed crown with clear cement had an imperceptible color change at the pre-/post-cementation phase. Meanwhile, the 10° and 20° milled crowns with regular cement exhibited unacceptable color changes after thermocycling. CONCLUSIONS For long‑term temporization in the aesthetic zone, properly optimized 3D-printed provisional restorations cemented with clear cement had better color stability.

Artificial intelligence systems in dental shade-matching: A systematic review

PURPOSE

Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry.

METHODS

A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI-based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies). A third investigator was consulted to resolve the lack of consensus.

RESULTS

Fifty-three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade-matching include fuzzy logic, a genetic algorithm with back-propagation neural network, back-propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K-nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object-detection applications, You Only Look Once-YOLO. Moment invariant was used for feature extraction.  XG (Xtreme Gradient) Boost was used in one study as a gradient-boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite-based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross-validation of 97%.

CONCLUSIONS

Lighting conditions, shade-matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge-based systems and neural networks have shown better accuracy in predicting dental shades.

Effect of Nano Ceramic Coating on Color Perceptibility and Acceptability of Polymethylmethacrylate: In Vitro and Clinical Study

The effect of a novel nano-ceramic coating (TiO₂) using an atomic layer deposition (ALD) technique on the surface of polymethyl methacrylate (PMMA) material was investigated. The patients' and clinicians' perception and acceptance of the PMMA color with TiO₂ coating were also examined. In vitro color measurement was performed on thirty specimens (light, original, and dark pink) before and after TiO₂ coating. Patients' and clinicians' perception and acceptance of color changes on PMMA were measured and compared. Descriptive and analytic statistics were analyzed (a = 0.05). TiO₂ films were successfully deposited on the PMMA specimen by the ALD technique. Color changes after TiO₂ coating were observed on all three PMMA shades, significantly higher than the established 50:50% perceptibility threshold, but below the established 50:50% acceptability threshold. The percentage of patients that perceived a color difference after TiO₂ coating were 83.3%, 63.9%, and 77.8% for light, original, and dark pink, respectively. The percentages of clinicians that were satisfied with the color difference were 96.4%, 80%, and 69.2% for light, original, and dark pink, respectively. Color changes after TiO₂ coating were observed, but below the acceptable threshold. The clinical survey demonstrated that a color difference was perceived but was clinically acceptable. In general, laypeople have lower perception and higher acceptance of changes in PMMA color than clinicians.

Effect of different coloring techniques and surface treatment methods on the surface roughness of monolithic zirconia

BACKGROUND

This study aimed to evaluate the effect of different coloring techniques and surface treatment methods on the surface roughness of monolithic zirconia ceramic.

MATERIALS AND METHODS

In this in vitro study seventy-two disk-shaped monolithic zirconia (2 mm × 10 mm) were divided into three coloring techniques groups (white, internal staining, external staining) (n = 24). Each group was subdivided into four surface treatment subgroups (n = 6), as unpolished, polished with Shofu polishing kit, polished with dental direct polishing kit, and glazed. Profilometer was used to measure the Ra (roughness average) and Rz (roughness height) surface roughness values (μm) and scanning electron microscopy (SEM) for visual inspection of the surface morphology. The surface roughness parameters were calculated and analyzed with two-way ANOVA and Tukey's post hoc test (α = 0.05).

RESULTS

The coloring technique, surface treatment method, and interaction of these two parameters significantly affected the Ra and Rz parameters (P < 0.05). Concerning the surface treatment, the Rz value was significantly higher in the unpolished subgroup, followed by the glazed and polished subgroups. However, the two polishing systems were not significantly different. The internal staining group had significantly higher Rz value than the other staining method, when the specimens were glazed or polished with Shofu kit. SEM showed multiple scratches in unpolished samples which were smoothened by glazing and specially by polishing.

CONCLUSION

Among all the studied surface treatment methods, the lowest surface roughness was observed in highly polished monolithic zirconia, which was even less than the glazed one. The internal staining method can create a rougher surface for some of the surface treatment methods.