Artificial Intelligence in Biomedical Applications of Zirconia

Luo, Feng; Hong, Guang; Wan, Qianbing

doi:10.3389/fdmed.2021.689288

Cited by 3 publications

(1 citation statement)

References 72 publications

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“…In the pursuit of enhancing dental implant materials, zirconia has drawn significant attention due to its superior biocompatibility, esthetically pleasing nature, high corrosion resistance, good mechanical properties, and absence of reported allergic reactions [ 6 ]. Furthermore, zirconia implants have been introduced to overcome esthetic and biological problems that can arise from titanium [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Titanium, Zirconia, and Ceramic Dental Implant Materials: A Comparative Analysis of Mechanical and Esthetic Properties

Peter,

Shah,

Simon

et al. 2024

Cureus

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Background: Dental implant materials play a pivotal role in the success of restorative dentistry. This study comprehensively compares the mechanical and esthetic properties of three commonly used dental implant materials: titanium, zirconia, and ceramic. Objective: This study aimed to provide insights into the suitability of titanium, zirconia, and ceramic for various clinical applications within implant dentistry. Methods: Ninety dental implants, 30 for each material, were selected based on their well-established usage in dental implantology. Mechanical properties, including tensile strength, modulus of elasticity, and fatigue resistance, were assessed using state-of-the-art testing machines. Esthetic properties, such as color stability and translucency, were scrutinized through immersion in staining solutions and spectrophotometer measurements. Fracture properties and biocompatibility were also evaluated. Results: Mechanical testing revealed that titanium exhibited the highest tensile strength (810 ± 55 MPa), while zirconia demonstrated the highest modulus of elasticity (208 ± 8 GPa). Titanium also displayed the greatest fatigue resistance (1,010,000 ± 95,000 cycles), whereas zirconia had the highest hardness (1190 ± 45 Vickers hardness number (VHN)). Esthetically, zirconia showed superior color stability (ΔE: 1.7 ± 0.2), while ceramic exhibited the highest translucency (TP%: 15.3 ± 1.7). Zirconia presented the lowest surface roughness (0.28 ± 0.04 μm). Conclusion: This study provides insights into potential dental implant material performance, with zirconia emerging as a promising alternative. Future research should validate these findings in clinical settings, considering a broader array of variables and long-term outcomes.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Titanium, Zirconia, and Ceramic Dental Implant Materials: A Comparative Analysis of Mechanical and Esthetic Properties

Peter,

Shah,

Simon

et al. 2024

Cureus

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Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review

Bonny,

Al Nassan,

Obaideen

et al. 2024

Algorithms

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Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI’s potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes.

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Multilayer Perceptron to Assess the Impact of Anatomical Risk Factors on Traumatic Dental Injuries

Khan¹,

Jindal

2022

Journal of Orofacial Sciences

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Introduction: Traumatic dental injuries (TDIs) are the public dental health concern, with variable prevalence reported worldwide. Although, TDI is not a disease rather, it is a result of various risk factors. This study was performed to assess the influence of anatomical risk factors such as accentuated overjet, overbite, molar relationship, and lip competency in determining the number of traumatized teeth per affected individual by using the advanced statistical method of multilayer perceptron (MLP) model of deep learning algorithm of artificial intelligence (AI). Materials and Methods: A cross-sectional study consisted of 1000 school children (boys and girls) of index age groups between 12 and 15 years selected through multistage sampling technique. Orofacial anatomical risk factors associated with TDI were statistically analyzed by MLP model of deep learning algorithm of AI using IBM SPSS Modeler software (version 18, 2020). Results: MLP method revealed results in terms of normalized importance as overbite (100%) was the strongest risk factor for the occurrence of TDI in number of teeth of affected participants, followed by molar relationship (90.2%), overjet (87.7%), and the lip competency was found as the weakest risk factor. Conclusion: Using the MLP as statistical method, overbite was found as the strongest anatomical risk factor in determining the number of traumatized teeth per affected individual as compared to molar relationship, overjet, and lip competence.

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Artificial Intelligence in Biomedical Applications of Zirconia

Cited by 3 publications

References 72 publications

Comprehensive Evaluation of Titanium, Zirconia, and Ceramic Dental Implant Materials: A Comparative Analysis of Mechanical and Esthetic Properties

Comprehensive Evaluation of Titanium, Zirconia, and Ceramic Dental Implant Materials: A Comparative Analysis of Mechanical and Esthetic Properties

Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review

Multilayer Perceptron to Assess the Impact of Anatomical Risk Factors on Traumatic Dental Injuries

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