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Background and Aim: Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. Methods: MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. Result: A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near‐infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. Conclusion: AI models, especially convolutional neural network (CNN)‐based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice. [ABSTRACT FROM AUTHOR]

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Halitosis, commonly known as oral malodor, is a multifactorial health concern that significantly impacts the psychological and social well-being of individuals. It is the third most frequent reason for individuals to seek dental treatment, after dental caries and periodontal diseases. For an in-depth exploration of the topic of halitosis, an extensive literature review was conducted. The review focused on articles published in peer-reviewed journals and only those written in the English language were considered. The search for relevant literature began by employing subject headings such as 'halitosis, oral malodor, volatile sulfur compounds, artificial intelligence, and olfaction' in databases such as PubMed/Medline, Scopus, Google Scholar, Web of Science, and EMBASE. Additionally, a thorough hand search of references was conducted to ensure the comprehensiveness of the review. After amalgamating the search outcomes, a comprehensive analysis revealed the existence of precisely 134 full-text articles that bore relevance to the study. Abstracts and editorial letters were excluded from this study, and almost 50% of the full-text articles were deemed immaterial to dental practice. Out of the remaining articles, precisely 54 full-text articles were employed in this review. As primary healthcare providers, dentists are responsible for diagnosing and treating oral issues that may contribute to the development of halitosis. To effectively manage this condition, dentists must educate their patients about the underlying causes of halitosis, as well as proper oral hygiene practices such as tongue cleaning, flossing, and selecting appropriate mouthwash and toothpaste. This narrative review summarises all possible AI olfaction in halitosis. [ABSTRACT FROM AUTHOR]

The importance of oral health for overall health makes it critical to establish proper oral hygiene practices in children early on. The traditional approaches to teaching children about dental health may not be successful since they may not be able to keep their interest. Metaverse technology offers a promising alternative, allowing for the design of engaging and immersive worlds that can effectively educate children about oral health. Despite the growing interest in the use of metaverse technology in healthcare, there is a lack of comprehensive reviews on its potential for oral health promotion in children. This review aims to fill this gap by providing an overview of the current state of metaverse-based oral health promotion for children, discussing its benefits and challenges, and highlighting its potential for improving children's oral health. By conducting this review, the authors hope to provide new information on the potential of metaverse-based oral health promotion for children and to contribute to the ongoing discussion on the use of metaverse technology in healthcare. This review may also provide valuable insights for dental organizations and practitioners interested in using metaverse technology to promote oral health and healthy living among children. [ABSTRACT FROM AUTHOR]

Background: Adipokine is a huge family of cytokines which are cell-signaling proteins secreted by adipose tissue released by white adipose tissue (WAT), and adipolin (FAM132A/CTRP12 gene) is the newest member added to the adipokine family. This research marks the first attempt to estimate and compare the gingival crevicular fluid (GCF) and serum levels of adipolin in healthy subjects and periodontitis patients with and without type-2 diabetes mellitus (T2DM). Methods: The study population consisted of 10 patients each with healthy subjects and periodontitis with and without T2DM. GCF and serum samples were collected from each patient before non-surgical periodontal therapy. All the samples underwent an enzyme-linked immunosorbent assay (ELISA) test with an antibody specific to adipolin. Results: The mean GCF and serum adipolin levels were high in Groups I and III compared with Groups II and IV. Comparison of adipolin levels between the groups showed no statistically significant difference either in GCF (p = 0.68) or serum (p = 0.85). The comparison between GCF and serum adipolin levels in Group IV showed a statistically significant difference (p < 0.031). The mean values showed a decrease in adipolin values as the disease rate progressed. A negative correlation was seen in serum and GCF adipolin with HbA1C. Conclusion: As the GCF and serum concentration of adipolin show a gradual positive relation with disease severity, within the limitations of the current study, it can be postulated adipolin could be a possible anti-inflammatory biomarker of periodontal disease. [ABSTRACT FROM AUTHOR]