Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Background

Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas.

Methodology

Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles.

Results

The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring.

Conclusions

Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.

States and hotspots in Helicobacter pylori research from 2002 to 2021: A bibliometric analysis

BACKGROUND

Recently, numerous publications on Helicobacter pylori (H. pylori) have been published, but bibliometric analyses on this research field are scarce. To address this gap, we conducted a bibliometric analysis to provide a comprehensive overview and to explore the current research states and hotspots in this field.

MATERIALS AND METHODS

Publications on H. pylori from 2002 to 2021 were retrieved from the Web of Science Core Collection database (WoSCC). Trends in publications and citations were analyzed using Excel 2021. VOSviewer and Citespace were used to perform bibliometrics analysis.

RESULTS

36,266 publications on H. pylori were retrieved from the WoSCC database. In general, we observed an increasing trend in the number of publications over the past 20 years. The United States was the most productive and influential country, with the largest proportion of both publications and total citations. Helicobacter, US Department of Veterans Affairs, and Graham, David were the most productive journals, institutions and authors, respectively. Further analysis the co-occurrence and burst detection of keywords revealed that the most common keywords were "Helicobacter pylori," "gastric cancer," and "gastritis," all keywords were divided into eight main clusters, and the most important current research hotspot was the relationship between H. pylori infection and the changes of gut microbiota.

CONCLUSIONS

The United States has been the most productive and influential country on H. pylori research, and H. pylori-related research remains an active research field. The relationship between H. pylori infection and the changes of gut microbiota is a research hotspot attracting significant attention.

Knowledge Mapping of Intracranial Aneurysm Clipping: A Bibliometric and Visualized Study (2001-2021)

BACKGROUND

Intracranial aneurysms (IAs) are common cerebrovascular diseases with high rates of mortality and disability. With the development of endovascular treatment technologies, the treatment of IAs has gradually turned to endovascular methods. However, because of the complex disease characteristics and technical challenges of IA treatment, surgical clipping still plays an important role. However, no summary has been performed of the research status and future trends in IA clipping.

METHODS

Publications related to IA clipping from 2001 to 2021 were retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and visualization study with the help of VOSviewer software and R program.

RESULTS

We included 4104 articles from 90 countries. The volume of publications on IA clipping, in general, has increased. The United States, Japan, and China were the countries with the most contributions. The University of California, San Francisco, Mayo Clinic, and the Barrow Neurological Institute are the main research institutions. World Neurosurgery and the Journal of Neurosurgery were the most popular journal and most co-cited journal, respectively. These publications came from 12,506 authors, of whom Lawton, Spetzler, and Hernesniemi had reported the most studies. The reports from the past 21 years on IA clipping can generally be divided into 5 parts: (1) characteristics and technical difficulties of IA clipping; (2) perioperative management and imaging evaluation of IA clipping; (3) risk factors for subarachnoid hemorrhage caused by rupture after IA clipping; (4) outcomes, prognosis, and related clinical trials of IA clipping; and (5) endovascular management for IA clipping. "Occlusion," "experience," "internal carotid artery," "intracranial aneurysms," "management," and "subarachnoid hemorrhage" were the major keywords for future research hotspots.

CONCLUSIONS

The results from our bibliometric study have clarified the global research status of IA clipping between 2001 and 2021. The United States contributed the most publications and citations, and World Neurosurgery and Journal of Neurosurgery can be considered landmark journals in this field. Studies regarding occlusion, experience, management, and subarachnoid hemorrhage will be the research hotspots related to IA clipping in the future.

Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.

Advances and trends in meningioma research over the last decade: A scientometric and visual analysis

Objective

We conducted a scientometric and visual analysis of meningioma studies in the past ten years and discussed the current status and trends of meningioma research to provide a reference basis for conducting relevant clinical practice or research.

Method

A search of the topic of meningioma in the Web of Science Core Collection database was conducted for January 2012-December 2021. The scientometric tools CiteSpace (version 5.8.R3), VOS viewer (version 1.6.17), and the Bibliometrix package of R software (version 4.2.1) were used to visualize and analyze the country of publication, institution, author, keywords, and cited literature of meningioma.

Results

A total of 10,397 documents related to meningioma were collected, of which 6,714 articles were analyzed. The annual analysis shows an increase in published articles, with an annual growth rate of 8.9%. 26,696 authors from 111 countries or regions were involved in publishing relevant studies. The country with the highest number of publications was the United States (1671), and the institution with the highest number of publications was the University of California, San Francisco (242). The keyword clustering of current studies can be grouped into five groups: meningioma characteristics and basic research, surgical treatment, radiation therapy, stereotactic radiosurgery, and management of complications. Keyword trend analysis shows that meningioma classification and molecular characteristics are emerging hotspots for meningioma research in recent years.

Conclusion

The scientometric and visual analysis demonstrated the research status and trends of meningioma. Over the past decade, meningioma research has focused on managing meningiomas with a predominance of surgical treatment and radiation therapy. At the same time, meningioma classification and molecular characteristics are emerging as current and possible research hotspots in the coming period.

Emerging trends and research foci of berberine on tumor from 2002 to 2021: A bibliometric article of the literature from WoSCC

Background: Cancer, also known as a malignant tumor, is caused by the activation of oncogenes, which leads to the uncontrolled proliferation of cells that results in swelling. According to the World Health Organization (WHO), cancer is one of the main causes of death worldwide. The main variables limiting the efficacy of anti-tumor treatments are side effects and drug resistance. The search for natural, safe, low toxicity, and efficient chemical compounds in tumor research is essential. Berberine is a pentacyclic isoquinoline quaternary ammonium alkaloid isolated from Berberis and Coptis that has long been used in clinical settings. Studies in recent years have reported the use of berberine in cancer treatment. In this study, we performed a bibliometric analysis of berberine- and tumor-related research. Materials and methods: Relevant articles from January 1, 2002, to December 31, 2021, were identified from the Web of Science Core Collection (WOSCC) of Clarivate Analytics. Microsoft Excel, CiteSpace, VOSviewer, and an online platform were used for the literary metrology analysis. Results: A total of 1368 publications had unique characteristics. Publications from China were the most common (783 articles), and Y. B. Feng (from China) was the most productive author, with the highest total citations. China Medical University (Taiwan) and Sun Yat-sen University (China) were the two organizations with the largest numbers of publications (36 each). Frontiers in Pharmacology was the most commonly occurring journal (29 articles). The present body of research is focused on the mechanism, molecular docking, and oxidative stress of berberine in tumors. Conclusion: Research on berberine and tumors was thoroughly reviewed using knowledge map and bibliometric methods. The results of this study reveal the dynamic evolution of berberine and tumor research and provide a basis for strategic planning in cancer research.

Global publication trends and research hotspots of curcumin application in tumor: A 20-year bibliometric approach

Malignant tumor is a disease caused by the imbalance of cell growth and proliferation mechanism, which seriously threatens human health and life safety. However, side effects and drug resistance are the key factors that limit the efficacy of anti-tumor drugs. Therefore, it is urgent and necessary to explore and unearth natural, safe and effective chemosensitizers in tumor researches. Curcumin is the main active ingredient in Curcuma, which has anti-inflammatory, anti-inflammatory and anti-oxidation effects, and has inhibitory effects on a variety of cancers. Bibliometric analysis is a scientific and quantitative method to assess the published articles, which can help researchers to find the development trends and the research hotspots of a specific research field, providing the development of future research for researchers. This study searched the Web Science Core Collection (woscc) for publications related to curcumin and tumors from January 1, 2001 to December 31, 2021. The specific characteristics of 1707 publications were analyzed by using Microsoft Excel software, CiteSpace, Vosviewer and online analysis platform of literature metrology. China had the largest number of published articles, with 579 publications. Aggarwal BB’s articles total citations and average citations were the most. PLoS One had the largest number of publications, with 32 publications. The current research focuses on “nanoparticles”, “delivery”, “micells” and “doxorubicin”. At present, nano based drug delivery system to improve the bioavailability of curcumin and thus to treat tumors will be the focus of future research.