Artificial intelligence in diabetic retinopathy: Bibliometric analysis

Poly, Tahmina Nasrin; Islam, Md. Mohaimenul; Walther, Bruno; Lin, Ming‐Chin; Li, Yu-Chuan

doi:10.1016/j.cmpb.2023.107358

Cited by 30 publications

(12 citation statements)

References 39 publications

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“…This can be attributed to various factors, including advancements in medical imaging technology, a heightened awareness of the significance of early detection in preventing vision loss, and the availability of extensive data sets for training and validating ML and DL models. [61][62][63] Furthermore, progress in ML and DL techniques, such as the integration of convolutional NNs with SVMs or RF to automatically extract features from retinal images and enhance pattern classification tasks, 22,64 has contributed to increased attention and productivity. The reader can refer to Table A in the Supplemental Appendix to observe the most common techniques found in each category.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

García-Jaramillo,

Luque,

León-Vargas

2023

J Diabetes Sci Technol

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Background: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain. Methods: The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. Results: A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. Conclusion: This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

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Section: Discussionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

García-Jaramillo,

Luque,

León-Vargas

2023

J Diabetes Sci Technol

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“…The bibliometric information for research reports was obtained from the Science Citation Index (SCI) Expanded database within the Web of Science Core Collection of Clarivate Analytics. This database is a rich collection of citation indexes that represents the connections between scholarly research articles from various fields, including sciences, social sciences, and arts and humanities ( Poly et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

A comprehensive bibliometric analysis of global research on the role of acrolein in Alzheimer’s disease pathogenesis: involvement of amyloid-beta

Jallow,

Nguyen,

Sanotra

et al. 2024

Front. Aging Neurosci.

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BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and behavioral decline. Acrolein, an environmental pollutant and endogenous compound, is implicated in AD development. This research employs bibliometric analysis to assess current trends and key areas concerning acrolein-AD interaction.MethodsThe Web of Science was used to extensively review literature on acrolein and AD. Relevant data were systematically gathered and analyzed using VOSviewer, CiteSpace, and an online bibliometric tool.ResultsWe identified 120 English publications in this specialized field across 19 journals. The Journal of Alzheimer’s Disease was the most prominent. The primary contributors, both in terms of scientific output and influence, were the USA, the University of Kentucky, and Ramassamy C, representing countries/regions, institutions, and authors, respectively. In this field, the primary focus was on thoroughly studying acrolein, its roles, and its mechanisms in AD utilizing both in vivo and in vitro approaches. A significant portion of the research was based on proteomics, revealing complex molecular processes. The main focuses in the field were “oxidative stress,” “lipid peroxidation,” “amyloid-beta,” and “cognitive impairment.” Anticipated future research trajectories focus on the involvement of the internalization pathway, covering key areas such as synaptic dysfunction, metabolism, mechanisms, associations, neuroinflammation, inhibitors, tau phosphorylation, acrolein toxicity, brain infarction, antioxidants, chemistry, drug delivery, and dementia. Our analysis also supported our previous hypothesis that acrolein can interact with amyloid-beta to form a protein adduct leading to AD-like pathology and altering natural immune responses.ConclusionThis study provides a broad and all-encompassing view of the topic, offering valuable insights and guidance to fellow researchers. These emerging directions underscore the continuous exploration of the complexities associated with AD. The analyses and findings aim to enhance our understanding of the intricate relationship between acrolein and AD for future research.

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“…Moreover, connected lines in the network maps depicted the relationships between these entities. Larger points and more robust lines indicated a higher number of articles and more substantial collaborative relationships, respectively [ 14 , 15 ].…”

Section: Methodsmentioning

confidence: 99%

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis

Wu,

Islam,

Poly

et al. 2024

Interact J Med Res

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Background Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. Objective The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. Methods We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. Results A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). Conclusions This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

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Artificial intelligence in diabetic retinopathy: Bibliometric analysis

Cited by 30 publications

References 39 publications

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

A comprehensive bibliometric analysis of global research on the role of acrolein in Alzheimer’s disease pathogenesis: involvement of amyloid-beta

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis

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