The pursuit of aesthetic excellence in dentistry, shaped by societal trends and digital advancements, highlights the critical role of precise shade matching in restorative procedures. Although conventional methods are prevalent, challenges such as shade guide variability and subjective interpretation necessitate a re-evaluation in the face of emerging non-proximity digital instruments. This systematic review employs PRISMA protocols and keyword-based search strategies spanning the Scopus^®, PubMed.gov, and Web of Science^TM databases, with the last updated search carried out in October 2023. The study aimed to synthesise literature that identified digital non-proximity recording instruments and associated colour spaces in dentistry and compare the clinical outcomes of digital systems with spectrophotometers and conventional visual methods. Utilising predefined criteria and resolving disagreements between two reviewers through Cohen’s kappa calculator, the review assessed 85 articles, with 33 included in a PICO model for clinical comparisons. The results reveal that 42% of studies employed the CIELAB colour space. Despite the challenges in study quality, non-proximity digital instruments demonstrated more consistent clinical outcomes than visual methods, akin to spectrophotometers, emphasising their efficacy in controlled conditions. The review underscores the evolving landscape of dental shade matching, recognising technological advancements and advocating for methodological rigor in dental research.

Objective: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. Materials and methods: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied. Results: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis. Conclusions: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = − 0.01 (10), mean difference = − 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm³) compared to desktop laser scanning (322.70 ± 40.15 mm³). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68–0.87, sensitivity of 1.00, precision of 0.50–0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.

This systematic review aims to evaluate the antimicrobial activity of α-mangostin derived from Garcinia mangostana against different microbes. A literature search was performed using PubMed and Science Direct until March 2022. The research question was developed based on a PICO (Population, Intervention, Control and Outcomes) model. In this study, the population of interest was microbes, α-mangostin extracted from Garcinia mangostana was used as exposure while antibiotics were used as control, followed by the outcome which is determined by the antimicrobial activity of α-mangostin against studied microbes. Two reviewers independently performed the comprehensive literature search following the predetermined inclusion and exclusion criteria. A methodological quality assessment was carried out using a scoring protocol and the risk of bias in the studies was analyzed. Reward screening was performed among the selected articles to perform a meta-analysis based on the pre-determined criteria. Case groups where α-mangostin extracted from Garcinia mangostana was incorporated were compared to groups using different antibiotics or antiseptic agents (control) to evaluate their effectiveness. A total of 30 studies were included; they were heterogeneous in their study design and the risk of bias was moderate. The results showed a reduction in microbial counts after the incorporation of α-mangostin, which resulted in better disinfection and effectiveness against multiple microbes. Additionally, the meta-analysis result revealed no significant difference (p > 0.05) in their effectiveness when α-mangostin was compared to commercially available antibiotics. α-mangostin worked effectively against the tested microbes and was shown to have inhibitory effects on microbes with antibiotic resistance.

Although numerous studies have demonstrated the benefits of incorporating filler particles into maxillofacial silicone elastomer (MFPSE), a review of the types, concentrations and effectiveness of the particles themselves was lacking. The purpose of this systematic review and meta-analysis was to review the effect of different types of filler particles on the mechanical properties of MFPSE. The properties in question were (1) tensile strength, (2) tear strength, (3) hardness, and (4) elongation at break. The findings of this study can assist operators, technicians and clinicians in making relevant decisions regarding which type of fillers to incorporate based on their needs. The systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 original articles from 1970 to 2019 were selected from the databases, based on predefined eligibility criteria by two reviewers. The meta-analyses of nine papers were carried out by extracting data from the systematic review based on scoring criteria and processed using Cochrane Review Manager 5.3. Overall, there were significant differences favoring filler particles when incorporated into MFPSE. Nano fillers (69.23% of all studies) demonstrated superior comparative outcomes for tensile strength (P < 0.0001), tear strength (P < 0.00001), hardness (P < 0.00001) and elongation at break (P < 0.00001) when compared to micro fillers (30.76% of all studies). Micro fillers demonstrated inconsistent outcomes in mechanical properties, and meta-analysis of elongation at break argued against (P < 0.01) their use. Current findings suggest that 1.5% ZrSiO₄, 3% SiO₂, 1.5% Y₂O₃, 2–6% TiO₂, 2–2.5% ZnO, 2–2.5% CeO₂, 0.5% TiSiO₄ and 1% Ag-Zn Zeolite can be used to reinforce MFPSE, and help the materials better withstand mechanical degradation.