(1) Background: Nano-hydroxyapatite (nHAp) has been reported to have a remineralizing effect on early carious lesions. The objective of this scoping review was to analyze the remineralization potential of nano-hydroxyapatite (nHAp)-containing dentifrices, by mapping the existing literature. (2) Methods: This review was performed using the PRISMA-ScR Checklist, which is an extension of the PRISMA Checklist for Systematic Reviews and Meta-Analyses. In this study, the population, concept, and context (PCC) framework was used to find relevant papers published between 2010 and 2021. Nano-hydroxyapatite (nHAp) and dentifrices containing nHAp as one of the ingredients were the two main concepts of the research question. MeSH phrases, keywords, and other free terms relevant to nano-hydroxyapatite and dentifrices were used to search the literature databases. (3) Results: Preliminary searches yielded 59 studies; the title and abstract screening results excluded 11 studies. The remaining studies were thoroughly reviewed by two reviewers on the basis of the inclusion and exclusion criteria. Finally, 28 studies were included, and 20 studies were excluded. Most of the studies that were included reported that when nHAp was used alone, it had many different effects, such as remineralization, caries prevention, less demineralization, brighter teeth, less pain, and remineralization of enamel after orthodontic debonding. (4) Conclusions: Dentifrices that contain nHAp offer a variety of therapeutic and preventative effects. Currently, there is insufficient evidence to support the efficacy of nHAp dentifrices in primary teeth. Additional long-term investigations using standardized protocols are required to reach decisive conclusions about the effects of nHAp dentifrices on primary and permanent dentitions.

Background and Objectives: The present systematic review and meta-analysis undertake a comparison of studies that examine the accuracy of robot-assisted dental implant placement in relation to static computer-assisted implant surgery (SCAIS), dynamic computer-assisted implant surgery (DCAIS), and freehand procedures. This study aims to provide a comprehensive understanding of the precision of robot-assisted dental implant placement and its comparative efficacy in relation to other placement techniques. Methods: The guidelines recommended by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were used to organize and compose this review. Four electronic databases (PubMed, Web of Science, Scopus, and Cochrane) were systematically searched for pertinent articles. Articles were selected following the inclusion and exclusion criteria. Qualitative and quantitative analyses of the selected articles were performed. Results: The initial electronic search resulted in 1087 hits. Based on the inclusion and exclusion criteria, five articles were selected for qualitative analysis, out of which three were considered for quantitative analysis. Three parameters were considered for accuracy evaluation (angular, coronal, and apical deviation). The mean angular deviation was −1.22 degrees (95% CI, −1.06–−1.39), the mean coronal deviation was −0.15 mm (95% CI, −0.24–−0.07), and the mean apical deviation was −0.19 mm (95% CI, −0.27–−0.10). Conclusions: The robotic implant system was found to have significantly lower angular deviations and insignificantly lower coronal and apical deviations compared to DCAIS. Within the limitations of this review, it can be concluded that robot-assisted implant placement in resin models permits higher accuracy compared to DCAIS and SCAIS systems. However, due to the limited number of comparative studies with high heterogeneity, the findings of this review should be interpreted with caution. Further research is necessary to confirm the clinical application of robotics in implant surgery.

Objectives: The purpose of the study is to evaluate the diagnostic accuracy of artificial intelligence (AI) software in detecting a common set of periodontal and restorative conditions, including marginal bone loss, dental caries, periapical lesions, calculus, endodontic treatment, crowns, restorations, and open crown margins, using intraoral periapical radiographs. Additionally, the study will assess how this AI software influences the diagnostic accuracy of dentists with varying levels of experience in identifying these conditions. Methods: A total of three hundred digital IOPARs representing 1030 teeth were selected based on predetermined selection criteria. The parameters assessed included (a) calculus, (b) periapical radiolucency, (c) caries, (d) marginal bone loss, (e) type of restorative (filling) material, (f) type of crown retainer material, and (g) detection of open crown margins. Two oral radiologists performed the initial diagnosis of the selected radiographs and independently labeled all the predefined parameters for the provided IOPARs under standardized conditions. This data served as reference data. A pre-trained AI-based computer-aided detection (“CADe”) software (Second Opinion^®, version 1.1) was used for the detection of the predefined features. The reports generated by the AI software were compared with the reference data to evaluate the diagnostic accuracy of the AI software. In the second phase of the study, thirty dental interns and thirty dental specialists were randomly selected. Each participant was randomly assigned five IOPARs and was asked to detect and diagnose the predefined conditions. Subsequently, all the participants were requested to reassess the IOPARs, this time with the assistance of the AI software. All the data was recorded using a self-designed Performa. Results: The sensitivity of the AI software in detecting caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 91.0%, 86.6%, 97.1%, 82.6%, 89.3%, 93.4%, 80.2%, and 91.1%, respectively. The specificity of the AI software in detected caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 87%, 98.3%, 99.6%, 91.9%, 96.4%, 99.3%, 97.8%, and 93.1%, respectively. The differences between the AI software and radiologist diagnoses of caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss were statistically significant (all p values < 0.0001). The results showed that the diagnostic accuracy of operators (interns and specialists) with AI software revealed higher accuracy, sensitivity, and specificity in detecting caries, PA lesions, restoration, endodontic treatment, calculus, and marginal bone loss compared to that without using AI software. There were variations in the improvements in the diagnostic accuracy of interns and dental specialists. Conclusions: Within the limitations of the study, it can be concluded that the tested AI software has high accuracy in detecting the tested dental conditions in IOPARs. The use of AI software enhanced the diagnostic capabilities of dental operators. The present study used AI software to detect a clinically useful set of periodontal and restorative conditions, which can help dental operators in fast and accurate diagnosis and provide high-quality treatment to their patients.

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.

Periodontal disease is an infection-driven inflammatory disease characterized by the destruction of tooth-supporting tissues. The establishment of chronic inflammation will result in progressive destruction of bone and soft tissue changes. Severe periodontitis can lead to tooth loss. The disease has complex pathogenesis with an interplay between genetic, environmental, and host factors and pathogens. Effective management consists of plaque control and non-surgical interventions, along with adjuvant strategies to control inflammation and disrupt the pathogenic subgingival biofilms. Recent studies have examined novel approaches for managing periodontal diseases such as modulating microbial signaling mechanisms, tissue engineering, and molecular targeting of host inflammatory substances. Mounting evidence suggests the need to integrate omics-based approaches with traditional therapy to address the disease. This article discusses the various evolving and future drug targets, including proteomics, gene therapeutics, vaccines, and nanotechnology in personalized periodontal medicine for the effective management of periodontal diseases.

The present study aims to compare the levels of micro-RNA-146a and micro-RNA-126 in oral subgingival plaque and coronary plaque from artery walls in patients with coronary artery disease who suffer from generalized periodontitis. A total of 75 participants were selected and grouped into three categories of 25 patients each: GP+CAD, GP, and HP groups. GP+CAD consisted of patients diagnosed with generalized periodontitis (GP) and coronary artery disease (CAD). The GP+CAD group was further divided into two groups—GP+CADa: where subgingival plaque samples were collected; GP+CADb group: where coronary plaque samples were collected while the patient underwent a coronary artery bypass grafting surgery. The GP group consisted of 25 patients diagnosed with only generalized periodontitis. The HP group consisted of 25 systemically and periodontally healthy controls. miRNA-146a and miRNA126 levels were assessed in subgingival plaque (SP) samples from all groups. Results revealed that miRNA-146a was expressed at higher levels and miRNA-126 was downregulated in the GP+CAD group. microRNAs in subgingival plaque samples showed a significant correlation with the coronary plaque samples in the GP+CAD group. miRNA-146a and miRNA-126 were present in coronary artery disease patients with periodontitis. These micro-RNAs may serve as risk biomarkers for coronary artery disease and generalized periodontitis.