Abdulrahman Almalki

Three-dimensional (3D) printing is increasingly used for denture base fabrication, but printer-dependent processing may alter the properties of the same resin. This in vitro study compared Lucitone Digital Print 3D Denture Base resin fabricated with two manufacturer-validated systems: Carbon M2 (continuous liquid interface production) and Asiga MAX UV (digital light processing) (n = 10 per printer per outcome). After standardized cleaning, post-curing, and 7 days of water storage at 37 ± 1 °C, flexural strength, fracture toughness, fracture work, Vickers hardness, and surface roughness were evaluated. Asiga specimens showed significantly higher flexural strength (83.5 ± 2.0 vs. 76.9 ± 1.2 MPa; p < 0.0001), fracture work (1853 ± 330 vs. 1452 ± 295 J/m ² ; p = 0.010), and Vickers hardness (12.5 ± 0.7 vs. 11.4 ± 0.6 kg/mm ² ; p = 0.007). Fracture toughness, expressed as Kmax, did not differ significantly between Carbon and Asiga specimens (2.91 ± 0.36 vs. 2.75 ± 0.27 MPa·m^0.5; p = 0.287), nor did surface roughness (1.72 ± 0.24 vs. 2.13 ± 0.84 µm; p = 0.283). Thus, the printer system influenced selected mechanical properties of an identical denture-base resin, highlighting the importance of considering the complete resin–printer–post-processing system when interpreting material performance.

This study proposes an automated system using deep learning-based object detection to identify implant systems, leveraging recent progress in self-supervised learning, specifically masked image modeling (MIM). We advocate for self-pre-training, emphasizing that its advantages when acquiring suitable pre-training data is challenging. The proposed Masked Deep Embedding (MDE) pre-training method, extending the masked autoencoder (MAE) transformer, significantly enhances dental implant detection performance compared to baselines. Specifically, the proposed method achieves a best detection performance of AP = 96.1, outperforming supervised ViT and MAE baselines by up to +2.9 AP. In addition, we address the absence of a comprehensive dataset for implant design, enhancing an existing dataset under dental expert supervision. This augmentation includes annotations for implant design, such as coronal, middle, and apical parts, resulting in a unique Implant Design Dataset (IDD). The contributions encompass employing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with patch embeddings, achieving substantial performance improvement in implant detection, and expanding possibilities through the labeling of implant design. This study paves the way for AI-driven solutions in implant dentistry, providing valuable tools for dentists and patients facing implant-related challenges.

Purpose This study aimed to provide a comprehensive national overview of the prosthodontic workforce in Saudi Arabia, focusing on its demographic composition, educational background, geographic distribution, and workforce trends. The findings are intended to support policymakers in postgraduate education planning, and equitable access strategies to prosthodontics care. Materials and methods A retrospective cross-sectional analysis was conducted using official data obtained from the Saudi Commission for Health Specialties (SCFHS) as of May 2025. The dataset included all registered practicing prosthodontists across Saudi Arabia. Key variables included gender, nationality, professional classification, practice region, and postgraduate training background. The prosthodontist-to-population ratio was calculated based on the 2024 national census data. Descriptive statistics were used to analyze the characteristics of the prosthodontic workforce. Results A total of 1,542 prosthodontists were identified across Saudi Arabia, resulting in a national ratio of 4.37 prosthodontists per 100,000 population. The workforce was predominantly male (65.8%), with females representing 34.2%. Non-Saudis accounted for 56.5% of the workforce, while Saudis made up 43.5%. Most prosthodontists were located in major metropolitan areas, with 34.1% practicing in Riyadh, 24.6% in Makkah, and 11.8% in the Eastern Region. The most common countries of postgraduate education were Egypt (29.3%), Saudi Arabia (27.3%), India (9.0%), and the United States (7.2%). The workforce was almost evenly divided between the public sector (48.3%) and the private sector (47.8%). Conclusion Saudi Arabia has witnessed steady growth in its prosthodontics workforce, marked by increased training capacity, diverse educational backgrounds, and encouraging participation of female practitioners. Despite this progress, challenges remain in achieving balanced gender representation, equitable regional distribution, and consistency in training standards. These findings highlight the importance of thoughtful workforce planning and the need to reassess classification systems and align training pathways with internationally recognized benchmarks. Such efforts will be essential to ensure high-quality, inclusive, and sustainable prosthodontics care across all regions.

This study introduces an automated system employing deep learning-based object detection to identify dental caries from dental radiographs, leveraging advancements in self-supervised learning, particularly masked image modeling (MIM). We emphasize the benefits of self-pre-training, particularly in scenarios where acquiring suitable pre-training data is challenging. Our proposed Masked Deep Embedding of Patches (MDEP) pre-training method, an extension of the masked autoencoder (MAE) transformer, significantly improves dental caries detection performance compared to traditional baselines. Key contributions include utilizing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with the reconstruction of patch embeddings, and achieving notable performance enhancements in caries detection. This research paves the way for AI-driven solutions in dentistry, offering valuable tools for dentists and patients in the early detection and management of dental caries. Additionally, we validate the robustness and generalizability of our proposed method using another dataset, CariesXrays. Our findings indicate that MDEP achieves higher precision and recall rates, providing a significant improvement in diagnostic accuracy. The implications of this study extend beyond dental caries detection, suggesting that self-supervised learning can be effectively applied to other areas of medical imaging where data scarcity is a significant challenge.

Background: This study evaluated and compared the effects of thermal aging on the color stability and mechanical properties of CAD/CAM polymers utilized for provisional restorations. Material and Methods: Three CAD/CAM polymers in this study: CAD-Temp (CAT), Everest C-Temp (CT), and PEEK (PK). Forty specimens of each material were randomly assigned to two subgroups. Subgroup A was immersed in distilled water for 24 h, whereas Subgroup B was subjected to 5000 thermal cycles. The color stability, flexural strength (FS), survival probability, and microstructures were evaluated following thermal cycling. Data analysis was conducted utilizing two-way ANOVA along with Tukey’s test. Results: The CAT (3.74 ± 0.39) and CT (3.51 ± 0.54) groups exhibited the highest color variations, while PEEK (2.95 ± 0.45) showed the lowest color change. The baseline groups showed that the CT group had the highest flexural strength value (p < 0.05). The flexural strength values of CAT and CT groups significantly decreased (p < 0.05) following thermal cycling. No significant decrease in FS was observed following thermal cycling in the Pk group (p = 0.16). Conclusions: The color measurement and flexural strength outcomes were significantly influenced by CAD/CAM materials and thermal cycling. The CT group demonstrated superior flexural strength compared to the other groups, both before and after thermal cycling. The PK group shows the lowest color change compared to other groups. Regardless of aging condition, C-Temp and PEEK materials recorded the highest survival probability, a 95% significance level compared to CAD-Temp.

Purpose: To assess the accuracy of intraoral scanners (IOSs) for final impressions using dual‐purpose scan jigs (DPSJs) and standard scan bodies (SBs). Materials and Methods: An edentulous typodont model with multiunit abutment analogs in the lateral incisor (L), premolar (P), and molar (M) regions was used for intraoral scanning following the manufacturer's recommended protocol. This process involved digitally scanning standard SBs and DPSJs, with the obtained data superimposed and compared against the reference model. Three‐dimensional (3D) evaluation software (Geomagic Control X) was then employed to identify any deviations in the data, followed by statistical analysis using ANOVA one‐way tests and Bonferroni post hoc tests to determine differences at each location. Results: The overall accuracy ranged from 21.8 to 16.8 µm for DPSJs and from 25.3 to 16.1 µm for SBs. Statistical analysis showed no significant differences between DPSJs and SBs across lateral incisor (L), premolar (P), and molar (M) locations. Specifically, SBs exhibited higher accuracy in lateral incisors (16.8 µm) compared to DPSJs (21.8 µm), but this difference lacked statistical significance. Similarly, in premolar locations, SBs were slightly more accurate (16.1 µm) than DPSJs (17.3 µm), with no statistical significance. Conversely, DPSJs showed slightly better accuracy in molar locations (17.3 µm) than SBs (19.1 µm), which was not statistically significant. Conclusion: The in vitro study concluded that intraoral digital scanning with DPSJs showed comparable accuracy to standard scan bodies for fully edentulous arches. Furthermore, the 3D deviation of implant positions using both DPSJs and SBs fell within clinically acceptable ranges.

This case report applies computer‐aided design and computer‐aided design manufacturing (CAD/CAM) technology in fabricating an implant‐retained monolithic overdenture for a patient with limited vertical restorative space. Traditionally, a vertical space of 9 to 12 mm is required for conventional overdentures; however, this patient presented with only 6 mm available. Through the integration of CAD/CAM, a novel solution was designed that optimized both function and esthetics within the constrained space.

The purpose of this study is to investigate the interchangeability of newly acquired, short-term used, and long-term used semi-adjustable articulators. Metal analogs of partially dentate maxillary and mandibular arches were mounted on twelve semi-adjustable Stratos 300 articulators, divided into three groups based on usage over time: New, short-term used (2 years) articulators. Each articulator was calibrated according to manufacturer guidelines, ensuring consistency. Occlusal contact forces were recorded using a T-scan III device with a pressure-mapping sensor. Maximum intercuspation (MIP) contact magnitudes were recorded by two calibrated examiners at 10-second intervals, ensuring reliable data collection across all conditions. In MIP, tooth #9 consistently exhibited the highest contact percentages across all articulator conditions, with percentages of 22.2% in the new articulator, 21.8% in the short-term used articulators, and 21.5% in long-term used articulators, indicating stable contact force distribution. Tooth #12 consistently showed the lowest MIP contact percentages, suggesting minimal occlusal force in this region due to its absence. Contact forces, measured at 10-second intervals, showed no significant differences among the different articulator conditions in MIP (p > 0.01). Within the limitations of this in vitro study, the calibrated Stratos 300 semi-adjustable articulator maintained superior accuracy over time. The study confirms that the calibrated Stratos 300 semi-adjustable articulators provide consistent and accurate occlusal measurements over time, regardless of their usage over time. How to cite this article: Almalki A, Kutkut N, Althubaitiy R, et al. Interchangeability of Contemporary Semi-adjustable Articulators Used Over Time: An In Vitro Study. J Contemp Dent Pract 2024;25(9):869-872.

Objective: New shade‐matching devices are available for clinical use, but their accuracy and reliability have not been investigated. This in vitro study compared the accuracy and reliability of a shade‐matching colorimeter (Optishade), a spectrophotometer (VITA Easyshade V), and visual shade matching in simulated clinical conditions.Material and Methods: Shade matching was conducted with two digital devices; (Optishade and Vita Easyshade) and the visual shade selection method, with a new 16‐tab Vita Classical shade guide. Shade tabs were placed within a gingival matrix and measurements were taken from the central area of each shade tab enclosed in a black box to simulate the oral cavity. Visual assessments were conducted independently by two examiners. For the reliability assessment, each shade tab was measured 20 times by each examiner. Accuracy was calculated as a percentage of the matching readings with the actual shade per each tab. Data were analyzed with the one‐way ANOVA test (α = 0.05) and Pairwise Comparison (Bonferroni Post hoc Test) to assess accuracy. The reliability evaluation was conducted by comparison between both examiners using mean and standard deviation; by a two‐tailed t‐test for the independent samples for all the three groups included in the study.Result: The shade matching accuracy of Optishade was 97.50%, Vita Easyshade 89.38%, and visual method 62.19%. Statistically, high significant difference in accuracy (p < 0.001) was found when comparing both Optishade and VITA Easyshade with the visual method. Additionally, there was statistically significant difference in accuracy (p < 0.05) when Optishade was compared to Vita Easyshade. The reliability of the Optishade was 99.6% and Vita Easy shade was 99.38%. Statistically, showed no statistically significant difference in the reliability of shade match obtained within the same group when comparing the readings of Examiner 1 and Examiner 2 (p > 0.05).Conclusion: The colorimeter demonstrated the highest accuracy, followed by the spectrophotometer, and the visual shade selection method. Both digital methods investigated in this study showed comparable reliability.Clinical Implication: The results of this study emphasize the clinical advantage of digital shade‐matching devices over visual shade‐matching. The colorimeter offers high accuracy for shade matching and should be considered to use for shade matching especially with restorations in the esthetic zone.