METHODS: A structured self-administered questionnaire was developed at Najran University and provided to the participants for data collection. The data collected included information on risk perception and incorporation of measures for protection against COVID-19 to gauge the attitude of dentists during this period. Also, clinical implementation of various protective measures was reviewed.
RESULTS: Of the n = 322 dentists that answered the questions, 50% were general dentists and 28.9% were dentists working at specialist clinics, while the remaining 21.1% of dentists were employed in academic institutions. Among the newer additions to the clinic, 36.3% of dentists answered that they had added atomizers to their practices, followed by 26.4% of dentists that had incorporated the use of UV lamps for sterilization. We found that 18.9% dentists were using HEPA filters in their clinics, while 9.9% of dentists were making use of fumigation devices to control the risk of infection. One-way ANOVA was also carried out to demonstrate that there was a statistically significant difference (p = 0.049) between groups of dentists utilizing HEPA filters, UV lamps, atomizers, and fumigation devices to prevent the spread of SARS-CoV2 across their workplaces.
CONCLUSION: Dentists are aware of recently updated knowledge about the modes of transmission of COVID-19 and the recommended infection control measures in dental settings. A better understanding of the situation and methods to prevent it will ensure that the dental community is able to provide healthcare services to patients during the pandemic.
Objectives: This study is aimed at determining the factors influencing the job satisfaction level among dentists. It is also aimed at evaluating how personal (age, gender) and professional (type, type of qualification, and year of practice) characteristics influence overall job satisfaction.
Methods: For data collection, a structured self-administered questionnaire was used, in which one part collected information on personal characteristics. At the same time, the other contained a questionnaire related to job satisfaction. The German validated version of the questionnaire had a 10-point Warr-Cook-Wall (WCW) scale developed by Warr et al. in 1979. Each item was rated on a 5-point Likert scale, with 5 representing excessive satisfaction and 1 representing extreme dissatisfaction.
Results: The results revealed that dentists in Saudi Arabia have a higher satisfaction level with "colleagues and fellow workers" (26.5%). The relation between the years of practice was highly significant. However, they were dissatisfied with their "income" (22.6%), and when compared with concerning age, gender, profession, and their practice years, this finding was highly significant.
Conclusion: A higher percentage of satisfaction was seen with the "fellow and colleague's workers" dimension. At the same time, "income" was the aspect with which the dentists showed extreme dissatisfaction.
INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
CONCLUSION: In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.
METHODS: From 500 patient CBCT scans, 787 maxillary premolar teeth were evaluated. The sample was divided by gender and age (10-20, 21-30, 31-40, 41-50, 51-60, and 61 years and older). Ahmed et al. classification system was used to record root canal morphology.
RESULTS: The most frequent classifications for right maxillary 1st premolars were 2MPM1 B1 L1 (39.03%) and 1MPM1 (2.81%), while the most frequent classifications for right maxillary 2nd premolars were 2MPM1 B1 L1 (39.08%) and 1MPM1 (17.85%). Most of the premolars typically had two roots (left maxillary first premolars: 81.5%, left maxillary second premolars: 82.7%, right maxillary first premolars: 74.4%, right maxillary second premolars: 75.7%). Left and right maxillary 1st premolars for classes 1MPM1 and 1MPM1-2-1 showed significant gender differences. For classifications 1MPM1 and 1MPM1-2-1, age-related changes were seen in the left and right maxillary first premolars.
CONCLUSION: This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic.