Journal of Prosthetic Dentistry最新文献

Statement of problem: The effect of errors in the cut-out and rescan procedure with dental dam isolation in restorative dentistry might be crucial for the clinical success of indirect restorations, but investigations are lacking.

Purpose: The purpose of this clinical study was to assess the scanning deviations of 4 intraoral scanners (IOSs) after the cut-out and rescan procedure with dental dam isolation and to compare 2 different computer software programs in the assessment of the deviations.

Material and methods: Twenty initial scans (prescans) were collected from 20 participants using 4 dental IOSs (TRIOS 3; 3Shape A/S, Cerec Primescan; Dentsply Sirona, iTero Element 5D; Align, iTero Lumina; Align). The 3-dimensional data were obtained from the right side of the mandible between the canine and the second molar area and recorded in standard tessellation language (STL) format. Then, the second premolar was cut on the screen of each IOS using the cutting tool in its software program, within 1 mm of the adjacent teeth. The dental dam was applied, and the same quadrant was rescanned by each IOS for each participant to allow the software program to overlap the 2 scans and fill in the cut-out area. The superimposition scan was recorded in STL format as the rescan data. The trueness of each scanner was assessed by overlapping the prescan and rescan data with Geomagic ControlX (3D Systems) and Oracheck (Dentsply Sirona) software programs to assess the root mean square (RMS) errors and the mean distance (MD) deviations, respectively. The deviations were assessed individually for the first premolar, second premolar, and first molar. Two-way ANOVA and Robust ANOVA with the median method were used for the statistical analyses (α<.05).

Results: The overall RMS errors ranged between 60 and 90 µm, and the overall MD deviations ranged between 80 and 200 µm. All scanners presented beyond 60 µm of RMS errors with significant differences between them for the overall comparisons (P=.001). The iTero Lumina presented the lowest RMS error (60 ±20), followed by iTero Element 5D (70 ±20), Primescan (70 ±30), and TRIOS 3 (80 ±30) (P=.001). Regardless of the scanner type, the second premolar had significantly the highest RMS error (90 ±30) (P<.001). Regarding the overall data, a very low agreement was observed between MD deviations and RMS errors (Cronbach alpha=.047).

Conclusions: The cut-out and rescan procedure with dental dam isolation may cause RMS errors ranging from 60 to 90 µm, which were below the clinically acceptable limits. The deviation occurred mainly in the cut-out area, while the adjacent teeth were less affected. The level of deviation may vary depending on the selected IOS. The Oracheck may not be a good replacement for the Geomagic ControlX regarding the assessment of the deviations between the 2 scans.

The prosthodontic management of a 62-year-old edentulous woman with a velopharyngeal defect following maxillectomy and radiation therapy is described. She presented with concerns about obturator stability, hypernasality, and fluid leakage from her pharyngeal obturator prosthesis. A new pharyngeal obturator and mandibular paralleling prosthesis were fabricated using a digital workflow by incorporating computer-aided design and computer-aided manufacturing (CAD-CAM) technology and conventional processing techniques. This approach reduced clinical visits while ensuring optimal fit, retention, and function. The definitive obturator restored speech clarity, improved deglutition, and enhanced patient comfort.

Statement of problem: The clinical success of implant-supported restorations bonded to titanium base (Ti-base) abutments depends on initial retention strength and durability. While zirconia and cobalt chromium (Co-Cr) alloys have been commonly used, limited evidence exists on how they interact with Ti-bases in terms of various resin cements, surface treatments, and mechanical aging.

Purpose: The purpose of this in vitro study was to assess the effect of material, Ti-base surface treatment, resin cement type, and mechanical aging on the pull-out strength of implant-supported screw-retained restorations.

Material and methods: A total of 144 restorations consisting of 72 4Y-TZP zirconia (Noritake Katana STML; Kuraray Noritake Dental Inc) and 72 complete metallic CoCr (Remanium Star CoCr powder; Dentaurum GmbH & Co. KG) were bonded to Ti-base abutments (EL 6043; Century Technologies, S.R.L) using 3 resin cements (RelyX Ultimate, Panavia V5, and ResiCem) and 2 surface treatments (half were airborne-particle abraded with 50-µm aluminum oxide particles (Al₂O₃) at 0.25 MPa for 10 seconds from 10 mm or untreated). Half of the specimens underwent mechanical aging via 1 200 000 cyclic loading at 70 N and 30 degrees, and the others were left unaged. Pull-out tests were performed using a universal testing machine. Data were analyzed using t tests, 2- and 3-way ANOVA, and Tukey HSD tests (α=.05).

Results: Co-Cr restorations exhibited significantly higher retention than zirconia across all conditions (P<.05). Airborne-particle abrasion and the use of ResiCem significantly improved retention (P<.001). Mechanical aging significantly reduced bond strength for all groups (P<.001). Significant interactions were observed between material, cement, and surface treatment.

Conclusions: Based on the findings of this in vitro study, retention strength and durability were significantly influenced by material, cement type, surface treatment, and mechanical aging. Airborne-particle abrasion and 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP)-containing hydrophobic resin cements enhanced bonding stability, particularly in Co-Cr restorations, while mechanical aging reduced retention across all groups.

Statement of problem: Screw loosening is a common mechanical complication in implant-supported restorations, yet early detection remains challenging because of the lack of noninvasive, quantitative assessment tools.

Purpose: This in vitro study aimed to evaluate the ability of resonance frequency analysis (RFA) combined with machine learning techniques to detect screw loosening in 3 implant-abutment connection designs: conical, internal tri-channel, and external hexagonal.

Material and methods: Three connection types were tested using 6 implants inserted into mandibular jaw models. RFA was conducted using a triaxial accelerometer under 7 loosening conditions with the frequency responses in the x, y, and z directions being recorded. Supervised learning (random forest) was used for binary (intact versus abnormal) and multiclass (7 classes) classification. Unsupervised learning was used to detect anomalies.

Results: The RFA revealed unique vibrational profiles for each connection design. The random forest classifier achieved 100% accuracy in binary classification and 83% to 92% in multiclass classification. Feature importance analysis highlighted the relevance of the triaxial vibration data. The convolutional autoencoder consistently identified screw loosening as early as 5 degrees.

Conclusions: RFA combined with machine learning reliably detected screw loosening across various types of implant-abutment connections. This method offers a noninvasive, quantitative tool for the early detection of mechanical complications in implant dentistry.