Materials and Methods: The present systematic review was carried out according to PRISMA guidelines. The search was carried out on PubMed/MEDLINE, Cochrane collaboration, Science direct, and Scopus scientific engines using selected MeSH keywords. The articles fulfilling the predefined selection criteria based on the fit and accuracy of removable partial denture (RPD) frameworks constructed from digital workflow (CAD/CAM; rapid prototyping) and conventional techniques were included.
Results: Nine full-text articles comprising 6 in vitro and 3 in vivo studies were included in this review. The digital RPDs were fabricated in all articles by CAD/CAM selective laser sintering and selective laser melting techniques. The articles that have used CAD/CAM and rapid prototyping technique demonstrated better fit and accuracy as compared to the RPDs fabricated through conventional techniques. The least gaps between the framework and cast (41.677 ± 15.546 μm) were found in RPDs constructed through digital CAD/CAM systems.
Conclusion: A better accuracy was achieved using CAD/CAM and rapid prototyping techniques. The RPD frameworks fabricated by CAD/CAM and rapid prototyping techniques had clinically acceptable fit, superior precision, and better accuracy than conventionally fabricated RPD frameworks.
OBJECTIVE: This study aimed to analyze xerostomia, ageusia and the oral health impact in coronavirus disease-19 patients utilizing the Xerostomia Inventory scale-(XI) and the Oral Health Impact Profile-14.
METHODS: In this cross-sectional survey-based study, data was collected from 301 patients who suffered and recovered from COVID-19. Using Google Forms, a questionnaire was developed and circulated amongst those who were infected and recovered from coronavirus infection. The Xerostomia Inventory (XI) and Oral Health Impact Profile-14 were used to assess the degree and quality of life. A paired T-test and Chi-square test were used to analyze the effect on xerostomia inventory scale-(XI) and OHIP-14 scale scores. A p-value of 0.05 was considered as statistically significant.
RESULTS: Among 301 participants, 54.8% were females. The prevalence of xerostomia in participants with active COVID-19 disease was 39.53% and after recovery 34.88%. The total OHIP-14 scores for patients in the active phase of infection was 12.09, while 12.68 in recovered patients. A significant difference was found between the mean scores of the xerostomia inventory scale-11 and OHIP-14 in active and recovered COVID patients.
CONCLUSION: A higher prevalence of xerostomia was found in COVID-19 infected patients (39.53%) compared to recovered patients (34.88%). In addition, more than 70% reported aguesia. COVID-19 had a significantly higher compromising impact on oral function of active infected patients compared to recovered patients.
MATERIALS AND METHODS: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
RESULTS: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
CONCLUSION: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
METHODS: An online well-structured and validated faculty self-perceived competency questionnaire was used to collect responses from medical faculty. The questionnaire consisted of four purposely build sections on competence in student engagement, instructional strategy, technical communication and time management. The responses were recorded using a Likert ordinal scale (1-9). The Questionnaire was uploaded at www.surveys.google.com and the link was distributed through social media outlets and e-mails. Descriptive statistics and Independent paired t-test were used for analysis and comparison of quantitative and qualitative variables. A p-value of ≤0.05 was considered statistically significant.
RESULTS: A total of 738 responses were assessed. Nearly 54% (397) participants had less than 5 years of teaching experience, 24.7% (182) had 6-10 years and 11.7% (86) had 11-15 years teaching expertise. 75.6% (558) respondents have delivered online lectures during the pandemic. Asynchronous methods were used by 61% (450) and synchronous by 39% (288) of participants. Moreover, 22.4% (165) participants revealed that their online lectures were evaluated by a structured feedback from experts, while 38.3% participants chose that their lectures were not evaluated. A significant difference (p