Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.

Background In the era of the internet, patients seek health information ahead of getting the required treatment. Dental implant, which is among the most sought dental treatments, is not an exception. Incorrect health related information may lead to harmful deeds, so this study sought to assess the quality of web-based Arabic health information on dental implants. Methods The following engines were searched: Google (http://www.google.com), Yahoo! (http://www.yahoo.com), and Bing (http://www.bing.com) on 13 January 2022 for specific Arabic terms on “dental implants”. The first 100 consecutive websites from each engine were analyzed for eligibility. The eligible websites were assessed using JAMA benchmarks tool, DISCERN tool, and HONcode. An online tool (including FKGL, SMOG and FRE) was used to assess readability of the websites. Results There were 65 eligible websites, of which only one (1.5%) was HONcode certified. Only 3 (4.5%) websites attained a high score (> 65 out of 80) based on DISCERN tool: The mean DISCERN score was 41.14 ± 12.64. The mean JAMA score was 1.69 ± 1.13; however, only five (7.6%) met all JAMA criteria. The main shortcomings were attributed to not meeting the “Attribution” (54 [83.1%]) and “Authorship” (43 [66.2%]) criteria. The mean grade level of FKGL score was 7.0 ± 4.5. The majority of the websites (60%) scored less than 7, indicating easy content to understand. The mean grade level of SMOG score required to understand a website’s text was 3.2 ± 0.6. Around 91% of the websites had reading ease scores ≥ 80, suggesting that the website’s content was easy to read. Conclusion Unfortunately, although readable, most of the easily accessible web-based Arabic health information on dental implants does not meet the recognized quality standards.

Objective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITEcomb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.

Background Suppression of tumorigenicity 2 (ST2) is a member of the interleukin (IL)-1 family and has 2 isoforms: ST2L, a transmembrane form, and ST2, a soluble form. IL-33 can act as an immune system alarm signal when released by damaged cells, which in turn activates other cells expressing the ST2 receptor. This can cause inflammatory cytokines to be released and produced, as well as trigger osteoclastogenesis. This study aimed to investigate the levels of soluble ST2 in gingival samples. Material/Methods The study population consisted of 30 individuals. The participants were divided into 3 groups: healthy participants, patients with periodontitis, and patients with periodontitis and diabetes mellitus. Periodontitis was determined using probing depth, clinical attachment loss, and gingival index. Patients with stage 2 to 4 periodontitis met the inclusion criteria. Gingival crevicular fluid (GCF) was collected for quantification of samples for ST2 levels by using an enzyme immunoassay. Results The mean±standard deviation of ST2 GCF concentrations was relatively high (558.87±68.99) in the group with periodontitis and diabetes mellitus, compared with that of the periodontitis group (452.06±54.18) and healthy group (252.82±87.9). Conclusions GCF ST2 values were found to be a marker of inflammatory activities. Thus, GCF ST2 could be a potential biomarker for the diagnosis of periodontitis as well as systemic diseases, such as diabetes mellitus. This pilot study was limited by a small number of participants. To confirm the associations, more large-scale investigations should be conducted.

Guided bone regeneration (GBR) is a reliable technique used to treat ridge deficiencies prior or during implant placement. Injectable-platelet rich fibrin (i-PRF) laced with a bone substitute (sticky bone) has heralded the way for advancing the outcomes of bone regeneration. This study evaluated the efficacy of sticky bone in horizontal ridge augmentation with and without collagen membrane. A total of 20 partially edentulous patients (Group-I n = 10; Group-II n = 10) that indicated GBR were included, and the surgical procedure was carried out. In Group-I, the sticky bone and collagen membrane were placed in ridge-deficient sites and Group-II received only sticky bone. At the end of 6 months, 20 patients (Group-I (n = 10); Group-II (n = 10)) completed the follow-up period. A CBCT examination was performed to assess changes in the horizontal ridge width (HRW) and vertical bone height (VBH). A statistically significant increase in HRW (p < 0.05) was observed in both groups with mean gains of 1.35 mm, 1.55 mm, and 1.93 mm at three levels (crest, 3 mm, and 6 mm) in Group-I and 2.7 mm, 2.8 mm, and 2.6 mm at three levels in Group-II. The intergroup comparison revealed statistical significance (p < 0.05) with respect to HRW and KTW (Keratinised tissue width) gains of 0.775 at the 6-month follow-up. Sticky-bone (Xenogenic-bone graft + i-PRF) served as a promising biomaterial in achieving better horizontal bone width gain.