Development of predictive algorithms for the wear resistance of denture teeth materials.

OBJECTIVES: To investigate the wear resistance of conventional, CAD-milled and 3D-printed denture teeth in vitro with simulated aging. To use the collected data to train single time series sample model LSTM and provide proof of concept. METHODS: Six denture teeth materials (Three Conventional; Double-cross linked PMMA (G1), Nanohybrid composite (G2), PMMA with microfillers (G3), CAD-milled (G4), two 3D-printed teeth (G5, G6) (Total n = 60) underwent simulation for 24 and 48 months of linear reciprocating wear using a universal testing machine (UFW200, NeoPlus) under 49 N load, 1 Hz and linear stroke of 2 mm in an artificial saliva medium. Single samples were parsed through Long Short-Term Memory (LSTM) neural network model using Python. To determine minimal simulation times, multiple data splits for training were trialled (10/20/30/40%). Scanning electron microscopy (SEM) was performed for material surface evaluation. RESULTS: 3D printed tooth material (G5) had the lowest wear resistance (59 ± 35.71 µm) whereas conventional PMMA with microfillers (G3) shown the highest wear rate (303 ± 0.06 µm) after 48 months of simulation. The LSTM model successfully predicted up to 48 months wear using 30% of the collected data. Compared with the actual data, the model had a root-mean-square error range between 6.23 and 88.56 µm, mean-absolute-percentage-error 12.43-23.02% and mean-absolute-error 7.47-70.71 µm. SEM images revealed additional plastic deformations and chipping of materials, that may have introduced data artifacts. CONCLUSIONS: 3D printed denture teeth materials showed the lowest wear out of all studied for 48 months simulation. LSTM model was successfully developed to predict wear of various denture teeth. The developed LSTM model has the potential to reduce simulation duration and specimen number for wear testing of various dental materials, while potentially improving the accuracy and reliability of wear testing predictions. This work paves the way for generalized multi-sample models enhanced with empirical information.

Optical and mechanical properties of conventional, milled and 3D-printed denture teeth.

OBJECTIVES: Investigate the effect of various liquids on the optical properties and Vickers hardness of conventional, milled and 3D-printed denture teeth. METHODS: Six different types of denture teeth (Maxillary anteriors of three different conventional teeth, Vivodent DCL, SR Phonares II, Vita Physiodens; milled teeth, IvotionDent; and two different 3D-printed teeth, Asiga DentaTooth and NextDent C&B MFH) were investigated (total n = 336). The labial surface of each specimen was prepared to a dimension of 10 × 5 × 3mm. Specimens were immersed in artificial saliva, coffee, red wine and denture cleaner with artificial aging to simulate denture use of 12 and 24 months in vivo. Measurements of translucency parameter (TP), shade change (ΔE), surface roughness (Ra) and Vickers hardness (VHN) were conducted at baseline and after artificial aging while immersed in the liquids at each timeframe. Data were statistically analysed by ANOVA and post-hoc test (SPSS Ver 27). Surfaces of specimens were analysed under scanning electron microscopy (SEM). RESULTS: Milled teeth had the highest overall translucency parameter (5.33 ± 0.76-7.3 ± 0.99). All materials had statistically significant change in translucency parameter and shade after 24 months simulated aging (p < 0.05), especially the milled and 3D-printed teeth (p < 0.01). Surface roughness of all materials were under plaque accumulation threshold Ra = 0.2 µm. At baseline, Vita Physiodens teeth (PMMA with microfillers) demonstrated the highest hardness (33.99 kgf/mm2±3.7), whereas both 3D-printed materials exhibited the lowest hardness (13.27 kgf/mm2±0.36-18.13 kgf/mm2±0.93). Artificial saliva, red wine and denture cleaner had a statistically significant impact (p < 0.05) on hardness of all materials (12.1 kgf/mm2±1.17-30.77 kgf/mm2±2.98). CONCLUSIONS: Milled teeth exhibited the best optical properties (highest overall translucency parameter and lowest shade change). Milled teeth were also the only material that showed colour change (ΔE values) within clinically acceptable limits. Denture cleaner had the most impact on optical and mechanical properties of all materials. Surface roughness and hardness of 3D-printed teeth had the most change after artificial aging.

Optical Properties and Color Stability of Denture Teeth-A Systematic Review.

PURPOSE: To systematically review past studies to determine the effect of various solutions on the color of denture teeth, thus answering the question in regards to which type of denture teeth has the best optical properties after exposure to various solutions. The method of measuring the color of artificial teeth was also evaluated as a secondary outcome. MATERIALS AND METHODS: A search of studies that quantitatively investigated the influence of immersion solutions on the color change of denture teeth was conducted. Ovid MEDLINE, PubMed and Scopus databases were searched from 1997 to April 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used during article selection. Data regarding the effect of immersion solutions, accelerated aging and surface treatments on color change were gathered. Methodologies used to assess optical properties were also summarized and compared. The modified CONSORT checklist was used to determine the risk of bias of past studied included in this review. RESULTS: One hundred thirty-three studies were identified after removing duplicates. Forty-one studies were selected for full-text analysis, and 35 remaining papers met the inclusion criteria and were therefore included in this systematic review. Thirty-two in vitro studies and 3 in vivo studies were included in the review. All studies reported that immersion in various solutions has a significant influence on the change in color and optical properties of denture teeth. However, the discoloration of denture teeth is still clinically acceptable in most studies. Exposure to various solutions also affected the translucency parameter of denture teeth. Most studies also investigated the surface roughness and hardness along with the optical properties, and reported that immersion cycles did not cause changes in surface roughness of denture teeth, while hardness was affected. The optical properties of PMMA denture teeth have been studied extensively, whereas that of CAD/CAM and 3D printed denture teeth is limited. CONCLUSIONS: Color stability of CAD/CAM milled denture teeth is comparable to conventional PMMA denture teeth. There are contradictory findings in terms of color stability of 3D printed denture teeth as compared to conventional PMMA denture teeth. Staining by coffee is worst among the common beverages and solutions investigated. Denture teeth can show color changes after immersion in staining beverages as early as one week. The degree of discoloration of denture teeth after immersion is time dependent, with the larger extent in the initial phase.