The aim of this systematic review was to assess the long term remineralizing potential of casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) only in paste form compared with fluoride varnish, and or placebo in both naturally occurring and post-orthodontic white spot lesions in vivo. The literature search covered the electronic databases: PubMed and Google scholar from 2005-2016. Only articles published in English were included. Randomized control trials in which CPP-ACP delivered by paste form were included. All studies which met inclusion criteria underwent two independent reviews. Two ninety five articles were identified from the search after excluding duplications. Abstracts of forty one articles were reviewed independently. Twenty nine articles were excluded after reading Full text articles were retrieved for fifteen relevant studies. After reviewing articles independently, three articles were excluded after full text reading. Finally twelve studies were selected based on the eligibility criteria. The remineralizing effect of CPP-ACP were compared with placebo and fluoridated toothpaste and fluoride varnish in randomized control trial. A high level evidence of remineralizing potential of CPP-ACP on naturally occurring white spot lesion and WSL post orthodontic treatment was found in comparison with placebo/fluoridated toothpaste and fluoride varnish without any statistically significant difference. Well-designed RCTs are, therefore, required to improve the level of evidence in this area.

Background: To systematically review the current literature investigating the association between oral health and acquired brain injury. Methods: A structured search strategy was applied to PubMed, Embase, Web of Science, and CENTRAL electronic databases until March 2017 by 2 independent reviewers. The preferred reporting items for systematic review and meta-analysis guidelines were used for systematic review. Results: Even though the objective was to assess the association between oral health and acquired brain injury, eligible studies focused solely on different forms of stroke and stroke subtypes. Stroke prediction was associated with various factors such as number of teeth, periodontal conditions (even after controlling for confounding factors), clinical attachment loss, antibody levels to Aggregatibacter actinomycetemcomitans and Prevotella intermedia. The literature showed no consensus on the possible association between gingivitis and stroke. Patients with stroke generally had poorer oral hygiene practices and oral health. Dental prophylaxis and professional intervention reduced the incidence of stroke. Conclusions: Overall, oral health and stroke were related. Periodontitis and tooth loss were independently associated with stroke. However, prevention and timely intervention may reduce the risk of stroke. Stroke was the main cerebral lesion studied in the literature, with almost no publications on other brain lesions.

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp–tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

Background: In recent years, there has been remarkable growth in AI-based applications in healthcare, with a significant breakthrough marked by the launch of large language models (LLMs) such as ChatGPT and Google Bard. Patients and health professional students commonly utilize these models due to their accessibility. The increasing use of LLMs in healthcare necessitates an evaluation of their ability to generate accurate and reliable responses. Objective: This study assessed the performance of LLMs in answering orthodontic-related queries through a systematic review and meta-analysis. Methods: A comprehensive search of PubMed, Web of Science, Embase, Scopus, and Google Scholar was conducted up to 31 October 2024. The quality of the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST), and R Studio software (Version 4.4.0) was employed for meta-analysis and heterogeneity assessment. Results: Out of 278 retrieved articles, 10 studies were included. The most commonly used LLM was ChatGPT (10/10, 100% of papers), followed by Google’s Bard/Gemini (3/10, 30% of papers), and Microsoft’s Bing/Copilot AI (2/10, 20% of papers). Accuracy was primarily evaluated using Likert scales, while the DISCERN tool was frequently applied for reliability assessment. The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42–0.44]. No heterogeneity was observed between the experimental group (ChatGPT-3.5, Gemini, and Copilot) and the control group (ChatGPT-4). However, most studies exhibited a high PROBAST risk of bias due to the lack of standardized evaluation tools. Conclusions: ChatGPT-4 has been extensively used for a variety of tasks and has demonstrated advanced and encouraging outcomes compared to other LLMs, and thus can be regarded as a valuable tool for enhancing educational and learning experiences. While LLMs can generate comprehensive responses, their reliability is compromised by the absence of peer-reviewed references, necessitating expert oversight in healthcare applications.

Oral diseases are the most prevalent chronic childhood diseases, presenting as a major public health issue affecting children of all ages in the developing and developed countries. Early detection and control of these diseases is very crucial for a child’s oral health and general wellbeing. The aim of this systematic review is to assess the performance of artificial intelligence models designed for application in pediatric dentistry. A systematic search of the literature was conducted using different electronic databases, primarily (PubMed, Scopus, Web of Science, Embase, Cochrane) and secondarily (Google Scholar and the Saudi Digital Library) for studies published from 1 January 2000, until 20 July 2022, related to the research topic. The quality of the twenty articles that satisfied the eligibility criteria were critically analyzed based on the QUADAS-2 guidelines. Artificial intelligence models have been utilized for the detection of plaque on primary teeth, prediction of children’s oral health status (OHS) and treatment needs (TN); detection, classification and prediction of dental caries; detection and categorization of fissure sealants; determination of the chronological age; determination of the impact of oral health on adolescent’s quality of life; automated detection and charting of teeth; and automated detection and classification of mesiodens and supernumerary teeth in primary or mixed dentition. Artificial intelligence has been widely applied in pediatric dentistry in order to help less-experienced clinicians in making more accurate diagnoses. These models are very efficient in identifying and categorizing children into various risk groups at the individual and community levels. They also aid in developing preventive strategies, including designing oral hygiene practices and adopting healthy eating habits for individuals.