Journal of Prosthetic Dentistry最新文献

Precise iris positioning is critical to achieving esthetic and symmetric ocular prostheses. This technique presents a smartphone-guided digital grid technique using a web-based application to overlay a dynamic facial grid. Frontal images captured on a smartphone allow accurate iris localization, which is transferred onto a wax pattern using a circular outline. This straightforward, reproducible method minimizes operator bias, enhances esthetic outcomes, and offers a practical approach to efficient ocular rehabilitation in clinical practice.

Statement of problem: The manual segmentation of anatomic structures in cone beam computed tomography (CBCT) scans such as the mandibular canal is time-consuming, operator-dependent, and prone to variability, limiting efficiency and consistency in implant planning. Whether automated CBCT segmentation is more accurate remains unclear.

Purpose: The purpose of this systematic review and meta-analysis was to synthesize evidence on deep learning, primarily U-Net architectures, for automatic segmentation of the mandibular canal in CBCT imaging. The segmentation accuracy and clinical relevance for dental implant planning was evaluated.

Material and methods: A systematic review and meta-analysis were conducted by following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines using the PubMed, Scopus, and the Cochrane Library databases. Eligible studies applied U-Net-based segmentation to CBCT scans for dental implants. Data on segmentation accuracy, Dice Similarity Coefficient [DSC], 95% Hausdorff Distance [HD], and Intersection over Union [IoU]), model architecture, validation strategies, and clinical applicability were extracted. Study quality was appraised using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and QUADAS-2 tools. A random-effects meta-analysis using logit-transformed means and standard errors were performed for DSC and IoU, with results back-transformed to the proportion scale, while 95% HD was analyzed on its raw scale. A sensitivity analysis of DSC was conducted on overlapping studies reporting both DSC and IoU to verify metric consistency, and publication bias was examined using the Egger regression test and the trim-and-fill method.

Results: Eight studies were quantitatively synthesized. The pooled DSC was 0.84 (95% CI: 0.68-0.92), IoU was 0.81 (95% CI: 0.50-0.95), and 95% HD was 0.89 mm (95% CI: -0.13 to 1.92 mm), all demonstrating high heterogeneity (I²>99%). Sensitivity analysis of DSC on overlapping studies confirmed internal consistency between DSC and IoU (pooled DSC=0.89; IoU=0.81). The Egger tests indicated no significant publication bias (P=.932 for DSC; P=.898 for IoU; P=.626 for 95% HD). Advanced architectures such as attention- and residual-based U-Nets showed superior accuracy, though external validation and explainability analyses were rarely reported.

Conclusions: U-Net-based deep learning models show a strong potential for automated CBCT segmentation of the mandibular canal, offering improved efficiency and accuracy. However, widespread clinical adoption requires standardized reporting, external validation, and explainability features to ensure trust and generalizability.

Statement of problem: Obturators are essential for rehabilitating maxillofacial defects, improving function, esthetics, and quality of life. However, a comprehensive bibliometric analysis to evaluate their use in dentistry is lacking.

Purpose: The purpose of this bibliometric review was to analyze the perspectives and global trends of research on obturators in dentistry.

Material and methods: A search was conducted in the Web of Science database in September 2024. Two independent reviewers selected articles in which the use of obturators in dentistry was the focus. The following data were collected: number and density of citations, year of publication, journal, impact factor, study design, themes, country, continent, institution, authors, and keywords. Collaborative networks were created using VOSviewer. Correlation among data was determined using the Spearman test.

Results: A total of 305 articles published between 1948 and 2024 were included, totaling 3482 citations. The Journal of Prosthetic Dentistry was the most frequent journal (n=26), and case reports (n=133) and observational studies (n=57) were the most common designs. Most studies aimed to describe techniques for obturator rehabilitation (n=68), focused on adults (n=58), used mucosa-supported prostheses (n=214), and addressed oncological conditions (n=120). The United States led in publications (n=60), and Sao Paulo State University stood out with 10 articles. "Maxillectomy" was the most frequent keyword (n=48). The Spearman test showed a weak positive correlation between citations and impact factor (P=.329; P<.05) and a moderate negative correlation with publication year (P=-.509; P<.001).

Conclusions: Research on obturators has grown over the past 77 years, primarily featuring clinical reports focused on technique descriptions and functional outcomes. North America, particularly the USA, has led in scientific contributions on the topic.

Statement of problem: Shade selection remains subjective, but digital shade selection methods and applications promise more consistent and objective results. However, consensus on which method provides the best agreement and consistency is lacking.

Purpose: The purpose of this clinical study was to investigate the agreement among traditional, digital, and artificial intelligence (AI)-assisted shade selection methods.

Material and methods: A total of 85 participants without caries, periodontal disease, or restoration in the left maxillary central incisor were recruited. Color analysis was performed using 4 different methods on the middle third of the left maxillary central incisor: traditional method (TM) (VITA 3D Master; VITA Zahnfabrik); spectrophotometer (S) (VITA Easyshade V; VITA Zahnfabrik); intraoral scanner (IOS) (TRIOS; 3Shape A/S); and AI-assisted application (ChatGPT-4). ChatGPT-4 was not designed as an AI for diagnostic purposes in clinical settings but was used experimentally for color selection and required human intervention. Color analysis was performed according to the VITA 3D Master color scale. The Cohen kappa method was used to analyze the agreement between 2 methods. The Fleiss kappa method was used to analyze the agreement among the 4 methods (α=.05).

Results: A statistically moderate agreement was found between TM and IOS (kappa=.421; P<.001), and a statistically low agreement was found between TM and AI (kappa=.064; P=.287). A slight yet statistically significant agreement was found among the 4 methods (Fleiss kappa=.071, P<.05).

Conclusions: Shade selection methods are changing and developing with the digitalization of dentistry. This study showed that the shade selection methods have very low agreement, highlighting the need for these methods to complement each other.

Statement of problem: Facial prostheses restore esthetics and improve the quality of life for individuals with complex facial defects associated with tumors, trauma, inflammation, cysts, and congenital abnormalities. Despite advances in silicone materials and 3- and 4-dimensional (3D and 4D) modeling technologies, the stable retention of facial prostheses-especially in mobile facial regions-remains a clinical challenge that compromises both function and user satisfaction. While 4D facial expression-based modeling improves fit, individual factors such as skin viscoelasticity may influence retention, yet which patient-specific traits are most relevant remains unclear.

Purpose: This study investigated whether facial skin viscoelasticity influenced optimal prosthesis design using 4D models aiming to establish objective criteria for personalized prosthesis fabrication and to enhance functional retention during facial motion.

Material and methods: Thirty healthy adult volunteers (11 men, 19 women) were recruited. Using 3D printing, 3 silicone prostheses reflecting 30%, 50%, and 70% smile expressions (S30, S50, S70) were fabricated using 4D morphing data. Each prosthesis was applied to the participant's right nasal wing, and retention was evaluated through a standardized "falling-off test" during facial movement. The prosthesis with the longest retention determined group classification (F30, F50, F70). To assess skin properties, a Cutometer was used to measure elasticity and viscosity at defined facial landmarks under controlled temperature and humidity. Statistical analyses, including t tests and ANOVA with Bonferroni correction, examined correlations among the F30, F50, and F70 groups (α=.05).

Results: The falling-off test showed a mean ±standard deviation prosthetic retention time of 11.55 ±4.85 seconds. Skin elasticity differed significantly between F30 and F50/F70 (R5; P<.001, R7; P=.002), whereas skin viscosity showed no significant differences (P=.448).

Conclusions: The results indicated that elasticity, rather than viscosity, significantly influenced prosthetic retention. Based on elasticity levels, an optimal 4D facial expression model was proposed: 30% expression change for low elasticity, 50% for medium elasticity, and 70% for high elasticity. These findings provided a framework for personalized prosthesis design, improving stability during facial movement and enhancing patient-specific fabrication approaches. As this study was conducted on healthy individuals, further investigation involving patients with facial defects is essential to validate clinical applicability and optimize prosthesis retention strategies.

Statement of problem: A mucosa-supported guide for complete arch implant-supported prostheses has been widely used to enhance the accuracy of implant placement. However, the flapless surgical protocol involves a gingiva punch, inevitably leading to a decrease in keratinized mucosa width. Whether a novel double-guide kit will preserve keratinized mucosa is unclear.

Purpose: The purpose of this prospective pilot clinical study was to evaluate a novel double-guide kit with the purpose of preserving keratinized mucosa in patients requiring complete arch implant-supported prostheses.

Material and methods: A total of 16 participants scheduled for complete arch implant-supported prostheses were enrolled and randomly allocated to the control or double-guide group. In the control group, a traditional 1-piece surgical guide with a flapless protocol was designed and used. The double-guide kit, comprising a fixation pin guide and a working guide, allowed the incision of the keratinized mucosa and elevation of a full-thickness flap. The width of keratinized mucosa was measured at 2 timepoint: before implant osteotomy and 3 months after implant placement. The accuracy of implantation was evaluated by superimposing presurgical design and postsurgical cone beam computed tomography (CBCT) scans. The t test and nonparametric test were used for the analysis of reduction volume and accuracy based on the data types (α=.05).

Results: Eight participants (10 jaws and 61 implants) were assigned to the control group, and 8 participants (11 jaws and 57 implants) to the double-guide group. All implants integrated satisfactorily with no loss during the follow-up of 6 to 26 months. At 3 months postoperatively, the mean ±standard deviation reduction of keratinized mucosa width in the control group was 1.8 ±1.3 mm on the buccal side and 1.5 ±1.7 mm on the lingual side, while that in the double guide group was 0.6 ±1.3 mm on the buccal side and 0.5 ±1.0 mm on the lingual side, revealing a significant statistical difference (P<.05). The implant accuracy was also better in the double-guide group (P<.05).

Conclusions: The novel double-guide kit significantly reduced keratinized mucosa damage compared with traditional guided implant procedures while ensuring precise implant placement; this should be advantageous for the long-term stability of complete arch implant-supported prostheses.

Statement of problem: Three-dimensional (3D) printing has been increasingly used in dentistry as an alternative to conventional restorative materials, but the mechanical, biological, optical, and long-term properties of 3D printed resins remain unclear.

Purpose: The aim of this systematic review was to synthesize the mechanical, optical, and biological properties of 3D printed resins intended for definitive restorations.

Material and methods: A Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-compliant search in the MEDLINE/PubMed, Embase, and Web of Science databases was performed following a registered protocol (PROSPERO CRD420250653401).

Results: Forty‑two eligible studies from 2020 to 2025 evaluated 7 resins. Crowntec (Saremco) generally showed the highest mechanical properties. The mechanical performance of all 3D printing resins, particularly in terms of flexural strength, surface roughness, and Vickers hardness, remained below that of most computer-aided design and computer-aided manufacturing (CAD-CAM) composite resin blocks. Optical data indicated clinically acceptable ΔE00 values without exposure to staining agents. Data on biocompatibility remained limited.

Conclusions: This systematic review highlighted the potential and current limitations of 3D printed resins for definitive restorations. They were found to exhibit favorable mechanical and optical properties and potentially favorable biological properties for definitive single-unit restorations, but they remain mechanically and optically inferior to CAD-CAM composite resin blocks.

Statement of problem: Despite advancements in implant dentistry, implant failure remains a significant challenge. The current application of cone beam computed tomography (CBCT) is a descriptive, not a predictive, assessment of failure risk. This diagnostic limitation requires research to refine CBCT-derived metrics for risk prediction.

Purpose: The purpose of this retrospective clinical study was to evaluate the predictive value of CBCT-derived mandibular radiomorphometric indices in distinguishing between successful and failed dental implants.

Material and methods: A retrospective analysis was performed on 301 CBCT scans of male patients with dental implants. The implants were categorized as successful or failed according to predetermined radiographic criteria. Two quantitative indices, computed tomographic mental index (CTMI) and computed tomographic index (CTI), and 1 qualitative index, computed tomographic cortical index (CTCI), were independently evaluated by 2 experienced radiologists. Intraobserver and interobserver reliability were assessed, and the relationships between these indices and dental implant outcomes were investigated. The Shapiro-Wilk test for normality guided the selection between parametric and nonparametric tests. The chi-squared test analyzed the categorical variable CTCI (α=.05).

Results: CTMI and CTI demonstrated strong intraobserver and moderate interobserver concordance. However, the CTCI exhibited moderate intraobserver and interobserver agreement. Radiographic signs of implant failure were found in 156 implants (51.8%). The quantitative indices CTMI and CTI could not discriminate between situations of implant success and failure. However, a significant difference was found between the qualitative CTCI scores of failed and successful implants (P=.011).

Conclusions: The qualitative index CTCI showed weak but significant predictive value for dental implant failure, while the quantitative indices CTMI and CTI could not predict implant outcomes. These findings indicate that compromised mandibular cortical integrity could be a contributing factor in determining implant outcomes.