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BACKGROUND The advent of digital impressions using computer-aided design and manufacturing technology (CAD/CAM) has simplified and improved the fabrication of implant prostheses in dentistry. The conventional impression has several drawbacks, including tray selection, material type, impression technique, impression disinfection, and cast model storage. The inaccuracies caused by distortion and contraction of impression material can be minimized with digital impressions. This study aimed to compare digital dental impressions of 10 working casts made using the Pindex laser removable die system to fabricate parallel drill channels vs 10 working casts made using the Di-Lok plastic tray removable die system. MATERIAL AND METHODS An implant master die with 2 dental implant analogs was fabricated. Ten working casts using the Pindex laser removable die system with parallel drill channels and 10 working casts using the Di-Lok plastic tray removable die system were fabricated. The working casts were scanned using an extra-oral laboratory scanner and the implant master model was scanned with an intra-oral scanner. RESULTS The properties of the casts made using the 2 systems were evaluated and analyzed with ANOVA and post hoc Tukey test. The mean horizontal linear distances between A1B1 (P<0.021), A2B2 (P<0.018), C1D1 (P<0.026), C2D2 (P<0.03), B1C1 (P<0.01), and mean vertical distances between B1A2 (P<0.015), C1D2 (P<0.001), B1B2 (P<0.028), and C1C2 (P<0.001) were significantly different between the Pindex system and Di-Lok tray system as compared to intra-oral scans. CONCLUSIONS Complete digital workflow with intra-oral scans were more than the partial digital workflow with extra-oral scans for the Pindex system and Di-Lok tray systems.
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Dental Implants , Models, Dental , Workflow , Computer-Aided Design , Denture, Partial, Fixed , Research DesignABSTRACT
Introduction Oral health has an impact on a person's general health, well-being, and quality of life. Due to their expertise and interactions with people, school staff members can actively contribute to the promotion of children's health if provided with the proper training. Methodology A cross-sectional survey was conducted to assess the "oral health-related quality of life" using the Oral Impact on Daily Performance (OIDP) instrument among the secondary school teachers of Modinagar, Uttar Pradesh. The study included a sample of 400 government secondary school teachers and 400 private secondary school teachers. The independent sample t-test was performed to assess the relationship between the mean of OIDP dimensions and the prevalence of dental caries. Any p-value less than 0.05 was considered significant. The data was analyzed using the SPSS version 20.0 software package (IBM Corp., Armonk, NY). Results In the present study, most of the participants (174 (43.5%) government teachers and 197 (49.2%) private teachers) were in the age group of 41-50 years. According to the Decayed, Missing, and Filled Teeth (DMFT) index, the mean decayed teeth present for government and private school teachers were 0.45 ± 0.503 and 0.41 ± 0.493, respectively, and the mean DMF scores for government and private teachers were 1.27 ± 0.736 and 1.03 ± 0.757, respectively. There was a positive correlation between the DMFT scores and the mean scores for the dimensions of the OIDP among both government and private secondary school teachers. Conclusion The poor dental health status of teachers' has a bad impact on everyday performance and academic work. The findings of the study highlight the need for oral health education and good oral health maintenance among school teachers since they are the ones who can easily influence the behavior of the children.
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INTRODUCTION: The current cancer trend in India has reported an alarming increase in cancer of the lip, throat, and oral cavity. Few dentists are aware that malignant neoplasms can also occur in the form of granulation tissue and periapical granulomas. However, most dentists agree that biopsies are essential to diagnose oral cancer. This makes the timely diagnosis of oral cancer dependent upon the histopathological examination of the granulation tissue by the dentist. AIM: The main aim of this study was to evaluate the knowledge and attitudes of dentists regarding the diagnosis of oral cancer through histopathological analysis of granulation tissue. METHOD: A cross-sectional study was conducted on dentists who were residents of Uttar Pradesh, India. Two hundred and fifty study participants enrolled to complete a self-structured questionnaire containing 15 closed-ended questions. The study was conducted over a period of three months, from June to August 2020. Descriptive statistics were analyzed using IBM SPSS Statistics for Windows, Version 21.0 (Released 2012; IBM Corp., Armonk, New York, United States, and the t-test was performed. A significance level of P ≤ 0.05 was used to determine the statistical significance of quantitative variables. RESULTS: The study findings revealed that only a small percentage of dentists (47.2%) were aware of the importance of biopsies in diagnosing oral cancer. Moreover, a minority (14.4%) had conducted biopsies and submitted samples for histopathological analysis. Of them, 10% were aware that in clinical practice, malignant tumors can also manifest as periapical granulomas, granulation tissue, gingivitis, and other conditions. CONCLUSION: Dentists should proactively submit any granulation tissue for histopathological examination. Oral healthcare providers must maintain a high level of suspicion, develop a range of potential or differential diagnoses for oral cancer, and take necessary measures to attain a definitive diagnosis. This may include considering a referral to a specialist for the treatment of oral cancer.
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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.
