Mahapatro, Abinash

Background: The integration of artificial intelligence (AI) in dental and maxillofacial radiology is revolutionizing diagnostic accuracy and clinical decision-making. This bibliometric analysis investigates the research landscape, emerging trends, and scholarly impact of AI applications in this specialized field. Methods: A comprehensive search was conducted on 25 December 2024 using the Web of Science Core Collection database. Data analysis tools, including VOSviewer, CiteSpace, and Biblioshiny, were employed to examine publication trends, global contributions, collaborative networks, and keyword dynamics. Results: The analysis revealed a marked increase in AI-related publications in dental and maxillofacial radiology, particularly from 2016 onward. The number of studies rose steadily, reaching 218 publications in 2024. The United States led in research output, followed closely by China and South Korea, with KU Leuven emerging as the top-contributing institution. Reinhilde Jacobs was identified as the most prolific author, while Medical Physics was the most cited journal. Co-citation analysis highlighted influential works by authors such as J.H. Lee and F. Schwendicke . Keywords including “artificial intelligence,” “deep learning,” “CBCT,” and “classification” dominated research discussions, reflecting the field’s evolving focus. Recent research trends emphasize advanced applications in segmentation, accuracy enhancement, and predictive modeling. Conclusion: AI has become integral to the advancement of dental and maxillofacial radiology, offering significant improvements in diagnostic precision and treatment planning. This study underscores the importance of staying abreast of AI innovations to enhance patient care and foster future research opportunities. Researchers and clinicians are encouraged to adopt AI-driven approaches to maximize clinical efficiency and outcomes.

Background: Metabolic-associated fatty liver disease (MAFLD) affects approximately 30% of the global population and is associated with obesity and metabolic syndrome. Anthropometric indices such as body mass index (BMI), waist circumference (WC), and weight are crucial markers for evaluating the progression of MAFLD. Curcumin, a bioactive compound with anti-inflammatory and antioxidant properties, has been investigated for its potential to improve metabolic outcomes. This umbrella review aimed to systematically evaluate the effects of curcumin supplementation on anthropometric indices in patients with MAFLD, considering epidemiological strength and power analysis. Methods: An umbrella review of meta-analyses from randomized controlled trials was conducted. PubMed, Scopus, and Web of Science were searched up to September 2024. Meta-analyses assessing curcumin’s effects on BMI, weight, and WC in MAFLD patients were included. Quality was assessed using the A Measurement Tool to Assess Systematic Reviews version (AMSTAR-2) tool, and heterogeneity was evaluated using the I 2 statistic. Statistical analyses were performed using R and Stata 18 software. Results: Curcumin supplementation had no significant effect on BMI (standardized mean difference [SMD]: −0.01; 95% confidence interval [CI]: −0.24 to 0.21, P = .91) with low power (1-β = 0.03) and a low epidemiological grade according to GRADE criteria. Similarly, curcumin showed no significant effect on WC (SMD: −0.27; 95% CI: −0.57 to 0.04, P = .08) with high power (1-β = 0.96) but low epidemiological strength. However, curcumin significantly reduced weight (SMD: −0.12; 95% CI: −0.19 to −0.04, P < .01) with moderate power (1-β = 0.56) and moderate epidemiological strength. No significant effect of dose or effect of supplementation duration was found. Conclusion: Curcumin supplementation may result in a statistically detectable but clinically modest reduction in body weight in patients with MAFLD, while showing limited or no efficacy on BMI and waist circumference. The epidemiological strength of the evidence varies across outcomes and is of low to moderate quality. Further well-designed, high-quality randomized controlled trials are needed to confirm the clinical relevance of curcumin’s effects on anthropometric indices.

Background: The application of artificial intelligence (AI) in cardiac imaging has rapidly evolved, offering enhanced accuracy and efficiency in the diagnosis and management of cardiovascular diseases. This bibliometric study aimed to evaluate research trends, impact, and scholarly output in this expanding field. Methods: A systematic search was conducted on 14 August 2024 using the Web of Science Core Collection database. VOSviewer, CiteSpace, and Biblioshiny were utilized for data analysis. Results: The findings revealed a significant increase in publications on AI in cardiovascular imaging, particularly from 2018 to 2023, with the United States leading in research output. England and the United States have emerged as central hubs in the global research network, highlighting their role in generating high-quality and impactful publications. The University of London was identified as the top contributing institution, while Frontiers in Cardiovascular Medicine was the most prolific journal. Keyword analysis highlighted machine learning, echocardiography, and diagnosis as the most frequently occurring terms. A time trend analysis showed a shift in research focus toward AI applications in cardiac computed tomography (CT) and magnetic resonance imaging (MRI), with recent keywords like ejection fraction, risk, and heart failure reflecting emerging areas of interest. Conclusion: Healthcare providers should consider integrating AI tools into cardiovascular imaging practice, as AI has demonstrated the potential to enhance diagnostic accuracy and improve patient outcomes. This study highlights the rising importance of AI in personalized and predictive cardiovascular care, urging healthcare providers to stay informed about these advancements to enhance clinical decision-making and patient management.

Objective: This study aims to conduct a bibliometric analysis of the triglyceride-glucose (TyG) index in relation to cardiovascular disorders. Methods: Data for the analysis were extracted from the Web of Science Core Collection database on 13 July 2024. We utilized VOSviewer, CiteSpace, and Biblioshiny tools for the analysis. Results: The study revealed a marked increase in research outputs on the TyG index in recent years, peaking with 137 publications in 2023. China emerged as the leading contributor, followed by the USA. The Chinese Academy of Medical Sciences and Peking Union Medical College were among the top contributing institutions. Shouling Wu and Shuohua Chen emerged as the leading authors, with the journal Cardiovascular Diabetology publishing the most articles on this topic. Keyword analysis identified “insulin-resistance” as the most frequently occurring term, followed by “risk.” Cluster analysis identified eleven key research areas, including “percutaneous coronary intervention,” “obesity indicators,” “arterial stiffness,” and “heart failure.” Conclusion: This bibliometric analysis highlights the expanding role of the TyG index in metabolic and cardiovascular research. Key clusters such as percutaneous coronary intervention, obesity indicators, arterial stiffness, heart failure, new-onset hypertension, predicting outcomes, and subclinical coronary artery disease emphasize its wide applicability across diverse clinical settings. The keyword “risk” was the most frequently occurring term, underscoring the importance of the TyG index in cardiovascular risk assessment, alongside its growing use in prognostic applications. These findings reflect the increasing recognition of the TyG index as a pivotal biomarker in cardiovascular medicine and encourage further exploration of its clinical integration.