Background/purpose Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. Materials and methods The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. Results AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. Conclusion These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Objectives. This review aims to evaluate the effect of orthodontic therapy on periodontal health. Data. Original articles that reported on the effect of orthodontic therapy on periodontal health were included. The reference lists of potentially relevant review articles were also sought. Sources. A literature search was conducted using the databases, Medline, EMBASE, Cochrane Library, Web of Science, Google Scholar, and Scopus databases for relevant studies. The search was carried out by using a combined text and the MeSH search strategies: using the key words in different combinations: “periodontal disease,” “orthodontics” and “root resorption.” This was supplemented by hand-searching in peer-reviewed journals and cross-referenced with the articles accessed. Articles published only in English language were included. Letters to the Editor, historical reviews and unpublished articles were not sought. Conclusions. Within the limitations of the present literature review, it was observed that there is a very close inter-relationship between the periodontal health and the outcome of orthodontic therapy.

Background/purpose In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis. Materials and methods The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000–February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included. Results AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). Conclusion The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.

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The objective of this paper was to evaluate the studies that have reported on psychological issues among dental students in Saudi Arabia and to develop coping strategies to overcome these mental health-related issues. The present systematic review is in accordance with the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search for the articles was carried out in the electronic databases by four independent researchers. The data search was performed in the electronic search engines like PubMed, Google Scholar, Web of Science, Scopus, Medline, Embase, Cochrane and Saudi Digital Library for scientific research articles published from January 2000 until December 2020. STROBE guidelines were adopted for qualitative analysis of six articles which met the eligibility criteria. The analysis of the literature revealed that most of the studies included were conducted in the past 8 years in different regions of Saudi Arabia. Findings of this systematic review clearly state that dental students in Saudi Arabia experience higher levels of depression, stress and anxiety and stress during their education period, with a higher stress for female students compared to male students. There is an urgent need to introduce interventional programs and preventive strategies to overcome the long-term effects.

In recent times, numerous scientific articles have been published on the risks of exposure to infectious microorganisms in dental care settings. The main mode of transmission of such infectious organisms is primarily through bioaerosols generated during routine dental procedures which put both dental care providers and their patients at an increased risk of exposure. Other frequent modes of infection transmission often reported include cross contamination and inadequate adoption of infection control protocols. The main objective of this article is to highlight the findings of those studies that have reported on the routes and modes of transmission of infectious organisms in dental settings, to report possibilities of cross contamination in dental care settings, and also to report any breach in adherence to infection control protocols in dental care settings. We also intend to emphasize on standard infection control protocols and strategies that need to be considered in dental care settings during disease outbreaks like coronavirus disease (COVID-19).

The outbreak of this corona virus disease 19 (COVID-19) was first reported in Wuhan city in China and now it has become a global issue. The total number of confirmed cases of COVID-19 recorded globally has surpassed 100,000. The mode of transmission of COVID-19 is usually from person to person and it could also be through direct transmission such as cough, sneeze, and inhalation of droplets. Dental care providers are at higher risk since they are more prone to cross contamination with the patient's saliva and aerosols that are released during the dental procedures. They can also be transmitted through other routes such as direct contact with blood, oral fluids, and from contaminated instruments. This paper intends to provide primordial-level preventive measures for dental care providers against this life-threatening COVID-19. How to cite this article Khanagar SB, Al-Ehaideb A, Naik S, et al. Primordial-level Preventive Measures for Dental Care Providers against Life-threatening Corona Virus Disease (COVID-19). Int J Clin Pediatr Dent 2020;13(2):176–179.

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