Journal of Prosthodontics-Implant Esthetic and Reconstructive Dentistry最新文献

This technique describes a scanner-agnostic digital workflow for generating dynamic mandibular motion from static virtual interocclusal records using a custom artificial intelligence (AI) algorithm and user interface. Virtual records of mandibular positions, including maximum intercuspation, protrusion, and lateral excursions, were captured with an intraoral scanner and processed through a custom interface developed using Python, an open-source, script-based programming language. The program interpolates intermediate positions using quantified point tracking and exports a motion path file compatible with dental computer-aided design software. By leveraging AI and open-source tools, this method offers a cost-effective, non-vendor-specific solution for integrating individualized jaw motion into digital prosthodontic workflows.

Purpose: The purpose of this study was to assess the dimensional trueness and fit of 3-unit monolithic zirconia restorations fabricated using various additive manufacturing (AM) techniques-namely, stereolithography (SLA), digital light processing (DLP), and lithography-based ceramic manufacturing (LCM)-in comparison with the computer numerical control (CNC) method.

Materials and methods: A total of 32 three-unit posterior fixed partial dentures (FPDs) were fabricated using 3 different additive AM methods (SLA, DLP, and LCM) and CNC as the control group. In all groups, 3 mol% yttria-stabilized tetragonal zirconia polycrystalline (Y-TZP) was used. The restorations, the restorations placed on the model, and the model itself were digitized. For the purpose of trueness and internal fit analysis, all STL datasets were imported into a high-precision metrology-grade 3-dimensional inspection software (Geomagic Control X 2022; 3D Systems) and virtually divided into four regions: intaglio, occlusal, axial, and marginal. Surface deviations were analyzed by using the root mean square (RMS) method, while the triple scan method was used for internal fit. Obtained data were then computed by using two-way ANOVA, followed by Bonferroni and Tukey post hoc tests (α = 0.05).

Results: SLA, CNC, and LCM provided similar and clinically acceptable marginal and internal trueness (p > 0.05). Conversely, the DLP method exhibited a significantly higher discrepancy in all regions, particularly in the marginal and intaglio surfaces (p ≤ 0.001). The lowest overall RMS deviation was observed in the SLA group (39.88 ± 4.84 µm), while the highest internal gap was found in the DLP group (218.29 ± 11.88 µm).

Conclusion: Additive manufacturing methods affected the fabrication trueness and fit of the 3-unit zirconia FPDs. Since the restorations produced by the DLP method had higher RMS and internal gap values, adjustment is required prior to clinical use.

Purpose: The aims of the present study were to (a) compare the scanning time and image count to complete optical scans of a typodont between augmented-reality-assisted intraoral scanning (ARIOS) and intraoral scanning (IOS), (b) compare the precision of the scan data derived from ARIOS and IOS, (c) compare participant-related outcomes between ARIOS and IOS, and (d) evaluate the effect of andragogical training in enhancing operator proficiency.

Materials and methods: A multisession within-subject experiment was conducted to compare ARIOS and IOS. Twenty-five dental students participated in the study. A training session was done to procure familiarity with both experimental conditions. The trial session consisted of each participant performing three sets of optical scans of a typodont under ARIOS and IOS. The time required to complete the scan and the number of images taken were recorded. Participant feedback was collected by means of entry, exit, and NASA-Task Load Index surveys. Precision of the scanned data was measured in root mean square (RMS).

Results: The present study observed a 7:3 preference for ARIOS at the exit survey. Scanning efficacy from ARIOS to IOS found an 8.5% decrease in mandibular scanning time, a 10.5% decrease in maxillary scanning time, an 8.6% decrease in mandibular scanning image count, and a 9.3% decrease in maxillary image count in favor of ARIOS. Precision of the scan data between ARIOS and IOS was comparable in RMS.

Conclusion: Andragogical practice of training enhanced participants' proficiency with ARIOS. ARIOS was advantageous compared to IOS in scanning efficacy, ergonomics, ease of use, and overall workload.

Purpose: The relationship between tooth color selection and individual satisfaction remains critical in dental esthetics. This study developed a hybrid approach combining deep learning with Bayesian network analysis to investigate how skin tone, age, and gender influence tooth color preferences.

Materials and methods: A total of 128 participants (62 males, 66 females; mean age 32.4 ± 7.8 years) evaluated 16 standardized smile images with different VITA classical shades. Participants included 60 dental professionals and 68 non-dental individuals. A hybrid model integrating convolutional neural networks with Bayesian networks was constructed to analyze color preferences. The model was validated through leave-one-out cross-validation and compared with traditional Bayesian analysis.

Results: The hybrid model achieved superior prediction accuracy (89.2%) compared to traditional Bayesian networks (82.5%, p < 0.01). B1, A1, and B2 shades received the highest overall ratings (8.63 ± 0.92, 8.21 ± 1.04, 7.85 ± 1.12, respectively). Males showed stronger preference for B1 shade (8.92 ± 0.78 vs. 8.37 ± 0.96 for females, p = 0.003), while females demonstrated more diverse preferences across multiple shades. Age negatively correlated with preference for lighter shades (r = -0.42, p < 0.001). Dental professionals exhibited greater discrimination between shades compared to non-dental participants (score range: 2.14-9.03 vs. 3.56-8.42, p < 0.01).

Conclusion: This novel hybrid framework significantly improved tooth color prediction accuracy compared to traditional methods. The findings provide quantitative guidance for personalized shade selection based on individual characteristics, potentially enhancing clinical outcomes and patient satisfaction in esthetic dentistry.

Purpose: This study aimed to develop a novel and efficient digital workflow for designing and manufacturing 3D-printed definitive maxillofacial obturator prostheses based on multi-source data fusion and various digital techniques, as well as to evaluate its feasibility through a randomized self-controlled study.

Materials and methods: Participants with maxillary defects were recruited for the study. A digital impression was obtained by fusing intraoral scanning data and computed tomography (CT) images. The framework and artificial-teeth-obturator were designed separately using multiple dental design software programs, fabricated through additive manufacturing (AM), and finally assembled precisely into one unit with the help of specially designed auxiliary positioning and connecting structures. For comparison, a conventional prosthesis was also made for each participant. The adaptation of both conventional and digital prostheses was evaluated and compared using the silicone rubber lining method through deviation analysis. Additionally, the chairside impression time for both conventional and digital treatments were recorded and compared.

Results: The digital workflow for designing and manufacturing maxillofacial prosthesis was successfully realized. The chairside impression time was shortened (p < 0.001). The adaptation of the digital prosthesis, including framework (349.89 ± 121.56 µm), obturator (420.08 ± 166.01 µm), and the whole prosthesis (408.36 ± 118.05 µm), proved suitable for clinical application. No statistically significant difference was observed between the digital and conventional prostheses.

Conclusion: The newly established digital workflow for the fabrication of definitive maxillofacial obturator prostheses reduced chairside impression time and the number of appointments, featuring clinically acceptable adaptation. This approach has potential for future applications in the treatment of patients with maxillofacial defects.

Purpose: This study aims to evaluate and compare the effect of the addition of two nanoparticles (silicon dioxide [Si-NPs] and hydroxyapatite [HA-NPs]) on the flexural strength and surface properties of three-dimensionally (3D)-printed denture base materials.

Materials and methods: In this study, Si-NP and HA-NP were added separately to two 3D-printed resins (ASIGA DentaBASE and NextDent Denture 3D+). The percentage of each nanoparticle was optimized at 0.25 and 0.50 wt%, with 3D-printed resins without nanoparticles (unmodified) as control groups. The specimens were thermally aged (5000 cycles at 5°C and 55°C). The flexural strength test of each group was conducted according to the ISO 20795-1:2013 specifications. After flexural strength, the fractured samples were analyzed under a scanning electron microscope (SEM). The surface roughness and Vickers microhardness tests were performed on disc-shaped (15 × 2 mm) samples. Data analysis was performed using ANOVA followed by Tukey's post hoc test at α = 0.05.

Results: The flexural strength was significantly increased with the addition of Si-NPs to ASIGA resin (p < 0.001), while there was no significant increase with Si-NP addition to NextDent resin. The highest flexural strength value (106.75 ± 8.59 MPa) was observed for 0.5% Si-NPs/ASIGA. The flexural strength was significantly decreased (p < 0.001) for both resins with the addition of two percentages of HA-NPs. The surface roughness was significantly reduced (p < 0.001) for both Si-NPs-based groups and 0.25%-based HA-NPs, whereas 0.50% HA-NPs/ASIGA showed the highest surface roughness value (0.46 ± 0.19 µm). Nonsignificant change in microhardness value was observed, whereby the NextDent group showed the highest values compared with ASIGA.

Conclusion: Overall, Si-NP had a positive impact on 3D-printed resins, resulting in an increase in flexural strength and a reduction in surface roughness compared to the HA-NP-based groups. However, no difference was observed in the microhardness testing.

Purpose: To evaluate the marginal gap, internal fit, and fracture resistance of screw-retained single implant 3Y-TZP and 5Y-TZP zirconia crowns with different cement gap thicknesses.

Materials and methods: Forty screw-retained single implant crowns, supported by forty implants and abutments, were constructed and divided into two groups (n = 20) according to the zirconia type (3Y-TZP vs. 5Y-TZP), and then into two subgroups according to the cement gap thickness, 40 and 60 µm. The cement gap initiation point was located 1 mm from the abutment's finish line, with a standardized zero end gap. Maxillary first premolar crowns were milled, and prefabricated screw access channels were used for the titanium-base group. ANOVA and Tukey tests were used to analyze the data.

Results: The 3Y-TZP40 (7.59 ± 0.28 µm) and 5Y-TZP40 (8.30 ± 0.50 µm) revealed less marginal gap (p < 0.001) compared to the 3Y-TZP60 (13.83 ± 0.15 µm) and 5Y-TZP60 (14.15 ± 0.59 µm). When the internal fit (µm) was evaluated, 3Y-TZP40 revealed a better internal fit compared to 3Y-TZP60 and 5Y-TZP60 in the axial area and total internal fit (p < 0.05). 3Y-TZP40 (2071.61 ± 115.89 N) and 3Y-TZP60 (2091.36 ± 44.12 N) revealed higher (p < 0.01) fracture resistance than 5Y-TZP40 (1634.80 ± 105.10 N) and 5Y-TZP60 (1655.32 ± 43.34 N). The two-way ANOVA revealed that cement gap thickness was a significant factor (p < 0.05) when measuring the marginal gap and axial and total internal fit. The type of zirconia was significant in the marginal gap and fracture resistance.

Conclusion: 3Y-TZP exhibits superior fracture resistance to 5Y-TZP. A thinner cement gap enhances marginal adaptation and internal fit, which can improve clinical longevity.

Purpose: To evaluate whether 3D intaglio-surface comparisons of elastomeric impressions can discriminate matching from non-matching implant connection families using root mean square error (RMSE) as an objective metric.

Materials and methods: A dataset of 25 unique implants was impressed with a scannable light body polyvinylsiloxane and digitized using a lab scanner (IdenticaT500, Medit, Seoul, Korea). Impressions captured implant platforms and internal anatomic details, and cross-comparisons using Geomagic Control X software generated 325 RMSE values. Bootstrap validation (SAS/STAT) tested whether values differed significantly for like and unlike implant groups. To validate the model, an impression was made of an implant in situ, and RMSE values were calculated using the original dataset.

Results: RMSE values ranged from 0 to 0.2049. Across 325 pairwise comparisons, intra‑group RMSE values were substantially lower than inter‑group values (means ∼0.037 vs. 0.129). ROC analysis supported an RMSE threshold of 0.0866 for discriminating between matches and non‑matches. The area under the ROC curve was 0.9761, indicating near-perfect discrimination between like and unlike implant groups. An RMSE of 0.0374 in the matched in situ case further validated the model.

Conclusions: RMSE values ≤ 0.0866 were statistically significant in predicting accurate implant identification. RMSE analysis offers an objective method to distinguish implant connection types and may complement radiographic assessment when prior records or optimal radiographic views are unavailable. Further work is needed to expand reference libraries for fixture‑level identification. This commonly encountered scenario has many clinical, professional, and ethical implications for routine maintenance and treatment of complications in implant dentistry.