Fracture Resistance Comparative Analysis of Milled-Derived vs. 3D-Printed CAD/CAM Materials for Single-Unit Restorations

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.

Influence of Novel SrTiO3/MnO2 Hybrid Nanoparticles on Poly(methyl methacrylate) Thermal and Mechanical Behavior

While dental poly methyl methacrylate(PMMA) possesses distinctive qualities such as ease of fabrication, cost-effectiveness, and favorable physical and mechanical properties, these attributes alone are inadequate to impart the necessary impact strength and hardness. Consequently, pure PMMA is less suitable for dental applications. This research focused on the incorporation of Strontium titanate (SrTiO3-STO) and hybrid filler STO/Manganese oxide (MnO2) to improve impact resistance and hardness. The potential of STO in reinforcing PMMA is poorly investigated, while hybrid filler STO/MnO2 has not been presented yet. Differential scanning calorimetry is conducted in order to investigate the agglomeration influence on the PMMA glass transition temperature (Tg), as well as the leaching of residual monomer and volatile additives that could pose a threat to human health. It has been determined that agglomeration with 1 wt% loading had no influence on Tg, while the first scan revealed differences in evaporation of small molecules, in favor of composite PMMA-STO/MnO2, which showed the trapping potential of volatiles. Investigations of mechanical properties have revealed the significant influence of hybrid STO/MnO2 filler on microhardness and total absorbed impact energy, which were increased by 89.9% and 145.4%, respectively. Results presented in this study revealed the reinforcing potential of hybrid nanoparticles that could find application in other polymers as well.