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

Artificial neural networks development in prosthodontics - a systematic mapping review

OBJECTIVES

This study aimed to systematically categorize the available literature and offer a comprehensive overview of artificial neural network (ANN) prediction models in prosthodontics. Specifically, the present research introduces a systematic analysis of ANN aims, data, architectures, evaluation metrics, and limitations in prosthodontics.

DATA

The review included articles published until June 2024. The search terms included "prosthodontics" (and related MeSH terms), "neural networks", and "deep learning". Out of 597 identified articles, 70 reports remained after deduplication and screening (2007-2024). Of these, 33 % were from 2023. Implant prosthodontics was the focus in approximately 29 % of reports, and non-implant prosthodontics in 71 %.

SOURCES

Data were collected through electronic searches of PubMed MedLine, PubMed Central, ScienceDirect, Web of Science, and IEEE Xplore databases, along with manual searches in specific journals.

STUDY SELECTION

This study focused on English-language research articles and conference proceedings detailing the development and implementation of ANN prediction models specifically designed for prosthodontics.

CONCLUSIONS

This study shows how ANN models are used in implant and non-implant prosthodontics, with various types of data, architectures, and metrics used for their development and evaluation. It also reveals limitations in ANN development, particularly in the data lifecycle.

CLINICAL SIGNIFICANCE

This study equips practitioners with insights, guiding them in optimizing clinical protocols through ANN integration and facilitating informed decision-making on commercially available systems. Additionally, it supports regulatory efforts, smoothing the path for AI integration in dentistry. Moreover, it sets a trajectory for future exploration, identifying untapped tools and research avenues, fostering interdisciplinary collaborations, and driving innovation in the field.

In vitro evaluation of the mechanical and optical properties of 3D printed vs CAD/CAM milled denture teeth materials

This laboratory research aimed to assess the Flexural strength, fracture toughness, Volumetric wear and optical properties of various recent 3D-printed denture tooth materials and compare them to CAD/CAM milled materials. Four 3D-printed denture tooth materials (Lucitone Tooth, OnX, Flexcera Ultra +, and VarseoSmile Crown Plus) and one CAD/CAM milled denture teeth material (Ivotion Dent) were used to fabricate fifteen specimens for each material (with total no. of 300 specimens). Tests were conducted according to ISO standards to assess flexural strength, fracture toughness, color staining, and volumetric wear. All materials were printed, washed, cured, or milled following the manufacturer's instructions. Flexural strength and fracture toughness values were obtained by a universal testing machine. Volumetric wear was evaluated using a non-contact optical profilometer. Color stability outcomes were obtained via a spectrophotometer for determining L*a*b* values, with color change (ΔE2000) based on the CIEDE2000 formula. Data were analyzed using one-way ANOVA and Tukey post-hoc analysis (α = 0.05). All 3D-printed materials exhibited higher flexural strength values than the milled material (p < 0.05). For fracture toughness, two of the 3D-printed materials showed higher values than the milled material, while the other two had lower values. Insignificant variances in volumetric wear were detected between the materials (p > 0.05). Color staining results varied, with milled materials generally demonstrating better-staining resistance compared to the 3D-printed materials. 3D-printed denture tooth materials exhibit good mechanical and optical properties, presenting a cost-effective and efficient alternative to CAD/CAM milled materials for denture tooth fabrication.

Evaluation of wear resistance and surface properties of additively manufactured restorative dental materials

OBJECTIVES

To evaluate the wear resistance of three additively manufactured dental crown materials (NextDent C&B MFH, Saremco print CROWNTEC and Bego VarseoSmile Crown) under two environmental conditions (dry and artificial saliva), two loads (49 N and 70 N) and two surface treatments (polished and glazed).

METHODS

A total of 120 specimens were divided into 24 groups and tested for wear under two loads (49 N and 70 N), surface treatment (polished or glazed), and environment (dry or submerged in artificial saliva). All samples underwent reciprocating wear testing at 1 Hz using a wear simulator, replicating 48 months of In Vivo conditions with a stainless-steel ball as the antagonist. The coefficient of friction (CoF), surface roughness, volumetric and vertical wear loss were measured and statistically analysed. Confocal microscopy assessed the surface properties of crown materials and the antagonists.

RESULTS

The NextDent material demonstrated the most homogenous wear, with relatively low vertical and volumetric loss across all groups (p < 0.004). NextDent and Bego materials performed similarly in artificial saliva regardless of the load type (p > 1.000). The CoF remained below 0.3 for all groups. All groups exhibited significant increases in surface roughness after testing, however, this did not correlate with an increase in the CoF. Confocal analysis revealed material deformities due to load and notable scratch marks on the stainless-steel antagonists.

CONCLUSION

It was found that all investigated addtively manufactured materials can be suggested for provisional use. Both vertical loss and volumetric loss results should be included for material evaluation. CoF and surface roughness should be implemented into wear evaluation.

CLINICAL SIGNIFICANCE

This study highlights the practical value of additively manufactured dental crown materials, particularly for provisional restorations. However, their extended use requires careful consideration of individual patient needs, emphasising the need for judicious clinical application evaluation.

Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review

Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.

Surface hardness and wear resistance of prefabricated and CAD-CAM milled artificial teeth: A cross-over clinical study

PURPOSE

To clinically evaluate the surface roughness and wear resistance of prefabricated and CAD-CAM milled acrylic resin teeth for complete dentures.

MATERIALS AND METHODS

In a cross-over study design, 10 completely edentulous patients were randomly included in this study and given two complete dentures. The first complete denture was made using prefabricated teeth, while the second was constructed using CAD-CAM milled teeth. Following insertion (T0), 3 months (T3), and 6 months (T6), the complete dentures were scanned. Utilizing 3D surface super-imposition techniques, the vertical (2D wear), and volumetric (3D wear) material loss were measured. The hardness of the teeth was evaluated at the time of denture insertion (T0) and then after 6 months (T6) of denture insertion by digital Vickers hardness tester. Statistical analysis was done using SPSS software. Paired groups were compared by paired t-test. Also, a repeated measure test was used. The significant difference was considered if p ≤ 0.05.

RESULTS

The time of denture function was linearly correlated with the wear of the prefabricated and CAD-CAM milled denture tooth. Prefabricated acrylic teeth had significantly more vertical and volumetric wear after 3 and 6 months, compared to CAD-CAM milled denture teeth where p-values were 0.01, 0.009, 0.003, and 0.024, respectively. Additionally, CAD-CAM milled teeth displayed significantly higher hardness values than prefabricated teeth both before and after 6 months of use where p-values were 0.001. After 6 months, all studied teeth showed a decrease in their hardness.

CONCLUSIONS

In terms of wear resistance and surface hardness, CAD-CAM milled acrylic resin teeth were superior to prefabricated acrylic resin artificial teeth once the complete denture functions.

Artificial teeth obtained by additive manufacturing: Wear resistance aspects. A systematic review of in vitro studies

Wear resistance is one of the properties that must be considered for maintaining the long-term functionality of artificial teeth in dental prostheses. This property can be altered by the method of tooth fabrication, the material, the chewing force, and the relationship to the antagonist tooth. This systematic review evaluated the wear resistance of artificial teeth obtained by the additive manufacturing method and aims to answer the question, "Do artificial teeth for dental prostheses obtained by additive manufacturing show wear resistance similar to prefabricated ones?" The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklist guidelines were followed with a customized search in Scopus, PubMed/Medline, Embase, Science Direct, and Google Scholar databases on August 30, 2023. The inclusion criteria were artificial teeth for dental prostheses in acrylic resin by additive manufacturing and comparing the wear resistance with conventional prefabricated teeth, in vitro and English studies, without time restriction. And excluded if 1) do not make artificial teeth by additive manufacturing or that were metal or ceramic teeth; 2) clinical trials, animal studies, review articles, case reports, letters to the editor, short communication, book chapters; 3) another language that is not English. The selection was in two steps, reading the titles and abstracts, followed by reading the selected studies in full. The risk of bias analysis was performed with the adaptation of the quasi-experimental studies tool by Joanna Briggs Institute. Four hundred and twelve articles were found in the databases, after the selection steps and application of eligibility criteria, 6 articles were included for qualitative data analysis and presented low risk of bias. For teeth obtained by additive manufacturing, 2 studies reported lower wear resistance, 2 studies had higher resistance, and 2 similar compared to prefabricated ones. Additive manufactured teeth compared to prefabricated teeth show influences on wear resistance due to differences in material composition, relationship to the antagonist's tooth, applied force, chewing cycles, and processing methods.