Purpose: To critically assess the unmet oral health needs and oral health-related quality of life among old age home inhabitants in Karnataka, India. Methods: A total of 96 older adults (males=32, 33.33%, females=64, 66.67%) aged 60 years and above, residing in old age homes were included in this cross-sectional study. The mean age of the participants included in the study was 69.25± 7.99 years. Oral health status and dental needs were assessed using the Oral Hygiene Index Simplified (OHIS) and the WHO Oral Health Assessment forms. Oral health-related quality of life was evaluated by administrating the Geriatric Oral Health Assessment Index (GOHAI) questionnaire through an interview format. Statistical Analysis: The Statistical Package for Social Sciences (SPSS) version 17 was used. Descriptive statistics was tabulated for oral health status and GOHAI scores. Student’s t-test and ANOVA were applied to check the association of the GOHAI responses with the number of years of residence in old age homes, age groups, gender, and dentition status. Results: The mean age of the participants included in the study was 69.25± 7.99 years. Twenty-six (27.1%) participants reported diabetes mellitus and hypertension. Most of the participants 89 (91.7%) visited the dentist when they had dental complaints. Among the dentulous study population, the majority of the participants had untreated dental caries indicated for dental extraction and needed periodontal therapy. Amongst the participants, 68 (70.82%) required prosthetic rehabilitation. More than 10 years of residence in old aged homes, over 80 years of age, being female, and completely edentulous groups showed lower total GOHAI scores compared to their counterparts. Conclusion: The majority of the participants showed unmet oral health needs and lower GOHAI scores indicating poor oral health-related quality of life.

Artificial intelligence is a computer system which can replicate human behavior and largely supports human actions and interpretation, but not replace human responses. Over the past few decades, the field of artificial intelligence (AI) has experienced phenomenal development and expansion. We are surrounded by several instances of AI. AI has applications in different branches of Dentistry. A total of 59 papers from an electronic search using Google Scholar and PubMed were used to create this narrative review. Artificial intelligence can be utilised for diagnosis, decision-making, treatment planning, early detection and prevention of oral disease, and finally result prediction by utilising cutting-edge technology in imaging. It shows how dentists can use it as a useful tool at various phases of clinical cases. The future of AI in dentistry appears to be outstanding with advancements in full artificial intelligence technology, dental assistance, and dental instructional tools. In order to help dental professionals better grasp AI as a tool to assist their work with enhanced efficiency, investigations need to be done to uncover patterns and foresee future related to oral health concerns. This review article attempts to highlight these points and lays an emphasis on how AI is driving dentistry in the present and will improve dental care in the future.

Background. Implants are being widely used as a treatment option and are considered the best line of treatment owing to their high level of predictability. However, over 5 years, 0–14.4% of dental implants have demonstrated peri‐inflammatory reactions associated with loss of crestal bone and ultimately loss of the implant. Peri‐implant diseases are categorized into peri‐implant mucositis and peri‐implantitis. There are a number of risk factors associated with these conditions, and the early detection of these possible risk factors, change in the treatment protocol whenever required along with a regular follow‐up, can ensure a better survival rate of dental implants. In the present study, an attempt has been made to evaluate the risk factors associated with peri‐implant diseases and also to correlate these risk factors to the prevalence of peri‐implant diseases using the formulated risk assessment model. Methodology. The risk assessment model was prepared based on existing literature explaining the risk factors for peri‐implant diseases. This study was conducted as a pilot study, and the method of complete sampling was used wherein all subjects in whom implants have been placed at the Department of Periodontology and Department of Prosthodontics were recalled and assessed. The model was then evaluated on patients in whom dental implants were placed and the implants were loaded for a year. A total of 13 subjects with 21 implants were assessed for the presence or absence of risk factors, and a score was given. Test of proportion and chi‐square test was done. Results and Discussion. Of the 21 implants assessed, 15 implants were found to be at low risk and 6 implants with moderate risk of peri‐implant disease. The number of implants with low risk was higher in implants with peri‐implant mucositis (25%) whereas the implants with moderate risk was higher in implants with peri‐implantitis (75%). This comparison was statistically significant with a p value of 0.022. Conclusion. This risk assessment tool can be used in the early detection of peri‐implant disease, and identifying the risk factor may help in the success rate of the implant survival.

Background To investigate and compare the effect of four commercially used dental cement at 24 hours, 48 hours,72 hours (h) and 6 days on the cellular response of human gingival fibroblast (HGF). Methods 3 cement pellet samples were made for each 4-test cement (n=12). The cement used for this study were zinc phosphate (ZP), zinc oxide non-eugenol (ZOE), RelyX U200 (RU200), and glass ionomer cement (GIC). The cytotoxicity of peri-implant tissues was investigated using one commercial cell line. All processing was done following International Organization for Standardization (ISO) methods 10993-5 and 10993-12 (MTT assay Test). Cell cultures without dental cement were considered as control. Standard laboratory procedures were followed to permit cell growth and confluence over 48 hrs after sub-cultivation. Before being subjected to analysis, the cells were kept in direct contact with the cement samples for the suggested time period. To validate the results the specimens were tested three times each. Cell death and inhibition of cell growth were measured quantitatively. Results were analyzed using 1-way ANOVA (a=0.05) followed by Tukey B post hoc test. Results The study showed the dental cement test material was cytotoxic. ZOE, ZP, GIC, and RU200 were cytotoxic in decreasing order, respectively, significantly reducing cell viability after exposure to HGF (p <0.001). Conclusions Within the limitations of this in-vitro cellular study, results indicated that HGF were vulnerable to the test the dental cement. The highest cytotoxicity was observed in ZOE, followed by ZP, GIC, and RU200.

Shade selection is a crucial factor influencing patient satisfaction with dental prostheses and the overall success of treatment. Conventional visual methods of shade selection are inherently subjective, regardless of the availability of dental records or photographs. This study aims to assess the prevalence, satisfaction, and preference of tooth shades in a cross-section of the local Indian population and to evaluate their correlation with age, gender, and skin color in a diverse sample of 120 participants visiting the outpatient department of this Institution. Methods A total of 120 participants, comprising 60 males and 60 females across four age groups (20-30, 30-40, 40-50, and 50-60 years), were visually evaluated using the VITA 3D Master shade guide. Participants also indicated their satisfaction with their current tooth shade and their preferred shade. Results Value 2 was the most prevalent existing shade (52.5%) and the most preferred across all groups, especially among individuals aged 41-50 years (p < 0.001). Satisfaction was highest among males aged 41–50 years. No significant association was found between skin color and tooth shade preference or satisfaction. Conclusions The results suggest that shades in the Value 2 category are universally preferred, offering a useful reference for shade selection in clinical practice.

Artificial intelligence is a computer system which can replicate human behavior and largely supports human actions and interpretation, but not replace human responses. Over the past few decades, the field of artificial intelligence (AI) has experienced phenomenal development and expansion. We are surrounded by several instances of AI. The most typical examples include Chat GPT, Alexa, Google Maps, autocorrect and text editors, e-payments, virtual travel booking agent, social media monitoring, gaming, including chess matches involving computers versus human chess masters, self driving cars, adaptive cruise control, parking assistance, and facial recognition for biometrics such as retinal scans and fingerprint scans. AI has applications in different branches of Dentistry. This review article attempts to highlight these points and lays an emphasis on how AI is driving dentistry in the present and will improve dental care in the future. A total of 59 papers from an electronic search using Google Scholar and PubMed were used to create this narrative review. Artificial intelligence can be utilised for diagnosis, decision-making, treatment planning, early detection and prevention of oral disease, and finally result prediction by utilising cutting-edge technology in imaging. It shows how dentists can use it as a useful tool at various phases of clinical cases. The future of AI in dentistry appears to be outstanding with advancements in full artificial intelligence technology, dental assistance, and dental instructional tools. In order to help dental professionals better grasp AI as a tool to assist their work with enhanced efficiency, investigations need to be done to uncover patterns and foresee future related to oral health concerns.