Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017

Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.

Medical long-tailed learning for imbalanced data: Bibliometric analysis

BACKGROUND

In the last decade, long-tail learning has become a popular research focus in deep learning applications in medicine. However, no scientometric reports have provided a systematic overview of this scientific field. We utilized bibliometric techniques to identify and analyze the literature on long-tailed learning in deep learning applications in medicine and investigate research trends, core authors, and core journals. We expanded our understanding of the primary components and principal methodologies of long-tail learning research in the medical field.

METHODS

Web of Science was utilized to collect all articles on long-tailed learning in medicine published until December 2023. The suitability of all retrieved titles and abstracts was evaluated. For bibliometric analysis, all numerical data were extracted. CiteSpace was used to create clustered and visual knowledge graphs based on keywords.

RESULTS

A total of 579 articles met the evaluation criteria. Over the last decade, the annual number of publications and citation frequency both showed significant growth, following a power-law and exponential trend, respectively. Noteworthy contributors to this field include Husanbir Singh Pannu, Fadi Thabtah, and Talha Mahboob Alam, while leading journals such as IEEE ACCESS, COMPUTERS IN BIOLOGY AND MEDICINE, IEEE TRANSACTIONS ON MEDICAL IMAGING, and COMPUTERIZED MEDICAL IMAGING AND GRAPHICS have emerged as pivotal platforms for disseminating research in this area. The core of long-tailed learning research within the medical domain is encapsulated in six principal themes: deep learning for imbalanced data, model optimization, neural networks in image analysis, data imbalance in health records, CNN in diagnostics and risk assessment, and genetic information in disease mechanisms.

CONCLUSION

This study summarizes recent advancements in applying long-tail learning to deep learning in medicine through bibliometric analysis and visual knowledge graphs. It explains new trends, sources, core authors, journals, and research hotspots. Although this field has shown great promise in medical deep learning research, our findings will provide pertinent and valuable insights for future research and clinical practice.

Application of three-dimensional printing in cardiovascular diseases: a bibliometric analysis

AIM

This paper aimed to explore the application of three-dimensional (3D) printing in cardiovascular diseases, to reach an insight in this field and prospect the future trend.

METHODS

The articles were selected from the Web of Science Core Collection database. Excel 2019, VOSviewer 1.6.16, and CiteSpace 6.1.R6 were used to analyze the information.

RESULTS

A total of 467 papers of 3D printing in cardiovascular diseases were identified, and the first included literature appeared in 2000. A total of 692 institutions from 52 countries participated in the relevant research, while the United States of America contributed to 160 articles and were in a leading position. The most productive institution was Curtin University , and Zhonghua Sun who has posted the most articles ( n =8) was also from there. The Frontiers in Cardiovascular Medicine published most papers ( n =25). The Journal of Thoracic and Cardiovascular Surgery coveted the most citations ( n =520). Related topics of frontiers will still focus on congenital heart disease, valvular heart disease, and left atrial appendage closure.

CONCLUSIONS

The authors summarized the publication information of the application of 3D printing in cardiovascular diseases related literature from 2000 to 2023, including country and institution of origin, authors, and publication journal. This study can reflect the current hotspots and novel directions for the application of 3D printing in cardiovascular diseases.

Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities

Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries' attention, constituting a vital part of the global geopolitical chessboard since humanity's energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords "biofuel" and "nanocatalyst" were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms' oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx).

Mapping knowledge landscapes and emerging trends of the links between osteoarthritis and osteoporosis: A bibliometric analysis

Background

Osteoarthritis has the characteristics of degenerative changes in articular cartilage and secondary osteoporosis, and it is a common chronic irreversible joint disease. In addition to affecting articular cartilage, subchondral bone, joint capsule and synovial membrane also undergo pathological changes during the development of the disease. Multiple studies have revealed that patients with osteoarthritis were found to have a significantly increased risk of osteoporosis, which also contributes to the progression of osteoarthritis. However, in the current existing studies, we found that no scholars have used bibliometric analysis in the study of the relationship between osteoarthritis and osteoporosis. From the perspective of bibliometrics, this study summarizes in detail the degree of cooperation between countries, research institutions, authors, and related journals in the field of osteoarthritis and osteoporosis research and their respective influence. In this way, the evolution of knowledge structure, the change of research focus and the hot topics with research potential in the future can be further visualized and analyzed.

Methods

Search the Web of Science core collection in Science Citation Index Expanded for articles and reviews of research on osteoarthritis and osteoporosis from 1998 to 2021. Bibliometric tools such as VOSviewer, CiteSpace, were be frequently used in our study. They are mainly used to analyze collaborations between countries, research institutions, and publication authors. Meantime, co-citation analysis of journals, co-occurrence analysis of keywords and subject categories will also be reflected in the study.

Results

According to the search strategy, 1,078 publications were included during the period 1998-2021. And the number of annual publications on the relationship between osteoarthritis and osteoporosis is on the rise. The United States has achieved the most and contributed the most in this field and the Boston University was the most prolific institution. For the statistical analysis of published publications, Reginster JY had the highest number of publications, while Felson DT had the highest co-citation frequency. Respectively, Osteoarthritis And Cartilage was the most productive journal in this area of research. The keywords "inflammation," "expression," and "mesenchymal stem cells" may also be the development trend and research hotspot of the future research direction in this field.

Conclusions

In our study, the relationship between osteoarthritis and osteoporosis was analyzed by using literature measurement. These analysis results can lead researchers to learn more directly about the trend in this area and provide guidance for determining popular research directions.

EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review

BACKGROUND

There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community.

METHODS

We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study.

RESULTS

Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning.

CONCLUSIONS

EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.

Building Information Modeling (BIM) Driven Carbon Emission Reduction Research: A 14-Year Bibliometric Analysis

Governments across the world are taking actions to address the high carbon emissions associated with the construction industry, and to achieve the long-term goals of the Paris Agreement towards carbon neutrality. Although the ideal of the carbon-emission reduction in building projects is well acknowledged and generally accepted, it is proving more difficult to implement. The application of building information modeling (BIM) brings about new possibilities for reductions in carbon emissions within the context of sustainable buildings. At present, the studies on BIM associated with carbon emissions have concentrated on the design stage, with the topics focusing on resource efficiency (namely, building energy and carbon-emission calculators). However, the effect of BIM in reducing carbon emissions across the lifecycle phases of buildings is not well researched. Therefore, this paper aims to examine the relationship between BIM, carbon emissions, and sustainable buildings by reviewing and assessing the current state of the research hotspots, trends, and gaps in the field of BIM and carbon emissions, providing a reference for understanding the current body of knowledge, and helping to stimulate future research. This paper adopts the macroquantitative and microqualitative research methods of bibliometric analysis. The results show that, in green-building construction, building lifecycle assessments, sustainable materials, the building energy efficiency and design, and environmental-protection strategies are the five most popular research directions of BIM in the field of carbon emissions in sustainable buildings. Interestingly, China has shown a good practice of using BIM for carbon-emission reduction. Furthermore, the findings suggest that the current research in the field is focused on the design and construction stages, which indicates that the operational and demolition stages have greater potential for future research. The results also indicate the need for policy and technological drivers for the rapid development of BIM-driven carbon-emission reduction.

Structural and Temporal Dynamics of Mesenchymal Stem Cells in Liver Diseases From 2001 to 2021: A Bibliometric Analysis

Background

Mesenchymal stem cells (MSCs) have important research value and broad application prospects in liver diseases. This study aims to comprehensively review the cooperation and influence of countries, institutions, authors, and journals in the field of MSCs in liver diseases from the perspective of bibliometrics, evaluate the clustering evolution of knowledge structure, and discover hot trends and emerging topics.

Methods

The articles and reviews related to MSCs in liver diseases were retrieved from the Web of Science Core Collection using Topic Search. A bibliometric study was performed using CiteSpace and VOSviewer.

Results

A total of 3404 articles and reviews were included over the period 2001-2021. The number of articles regarding MSCs in liver diseases showed an increasing trend. These publications mainly come from 3251 institutions in 113 countries led by China and the USA. Li L published the most papers among the publications, while Pittenger MF had the most co-citations. Analysis of the most productive journals shows that most are specialized in medical research, experimental medicine and cell biology, and cell & tissue engineering. The macroscopical sketch and micro-representation of the whole knowledge field are realized through co-citation analysis. Liver scaffold, MSC therapy, extracellular vesicle, and others are current and developing areas of the study. The keywords "machine perfusion", "liver transplantation", and "microRNAs" also may be the focus of new trends and future research.

Conclusions

In this study, bibliometrics and visual methods were used to review the research of MSCs in liver diseases comprehensively. This paper will help scholars better understand the dynamic evolution of the application of MSCs in liver diseases and point out the direction for future research.

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis

BACKGROUND

Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress.

OBJECTIVE

This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity.

METHODS

We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references.

RESULTS

In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines.

CONCLUSIONS

This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

Global Trends in Atherosclerosis Research in the Epigenetics Field: Bibliometric and Visualization Studies

Atherosclerosis is a pathological vascular state caused by the interaction of environmental and hereditary factors. Epigenetic modifications may be the bridge connecting environmental factors and genetic factors. A search for publications on the Web of Science database in the field of atherosclerosis related to epigenetics was conducted from the earliest mention to 31 December 2020. Data on total and annual publications, citations, impact factors, Hirsch (H)-index, citation times, most prolific authors, and frequently published journals were collected for quantitative and qualitative comparison. A total of 1848 publications related to epigenetics and atherosclerosis were found. The major contributing countries were the China (522, 28.23%), United States (485, 26.23%), and Germany (119, 6.44%). The greatest number of retrieved publications were published in the journal, "Arteriosclerosis, Thrombosis, and Vascular Biology" (62, 3.66%). The publication "Oxidative Stress and Diabetic Complications" was cited 2370 times. The most frequent keywords were "DNA methylation" and "LncRNA". Publications on epigenetic research in the atherosclerosis field have increased significantly every year, indicating that the study of epigenetic modifications plays an increasingly important role in understanding the pathology of atherosclerosis.

Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis

This study investigates how wireless sensor network (WSN) applications in agriculture are discussed in the current academic literature. On the basis of bibliometric techniques, 2444 publications were extracted from the Scopus database and analyzed to identify the temporal distribution of WSN research, the most productive journals, the most cited authors, the most influential studies, and the most relevant keywords. The computer program VOSviewer was used to generate the keyword co-occurrence network and partition the pertinent literature. Findings show the remarkable growth of WSN research in recent years. The most relevant journals, cited countries, and influential studies were also identified. The main results from the keyword co-occurrence clustering and the detailed analysis illustrate that WSN is a key enabler for precision agriculture. WSN research also focuses on the role of other technologies such as the Internet of Things, cloud computing, artificial intelligence, and unmanned aerial vehicles in supporting several agriculture activities, including smart irrigation and soil management. This study illuminates researchers’ and practitioners’ views of what has been researched and identifies possible opportunities for future studies. To the authors’ best knowledge, this bibliometric study represents the first attempt to map global WSN research using a comprehensive sample of documents published over nearly three decades.

Surgical Procedures for Hip Joint Preservation for Osteonecrosis of the Femoral Head: A Bibliometric Analysis

Background and Objective. Studies have concentrated on the surgical procedures for hip joint preservation for osteonecrosis of the femoral head (ONFH). This study is aimed at presenting a bibliometric analysis of the relevant articles published from 1999 to 2019. Method. Articles which concentrated on surgical procedures for hip joint preservation for ONFH were searched using Web of Science database. The data were analyzed by using bibliometric analysis. Additionally, VOS viewer software was used for bibliographic coupling, coauthorship, cocitation, and cooccurrence analyses and to investigate the publication trends of the mentioned field. Results. A total of 3467 articles were included. China had the highest number of relevant published articles. However, the USA made the highest contributions to the global research with the highest citations and $h$ -index. The journal of Clinical Orthopaedics and Related Research published the highest number of relevant articles. Studies could be classified into four clusters: “process and clinical treatment,” “risk factors and diagnosis,” “pathophysiology,” and “basic research.” “Pathophysiology” and “basic research” clusters were predicted as the next hot topics of surgical procedures for hip joint preservation for ONFH. Conclusion. Based on the current global trends, the number of published articles related to surgical procedures for hip joint preservation for ONFH has increased. The USA was noted as the leading country in global research in the target field. “Pathophysiology” and “basic research” clusters may be the next hot spots, and scholars need to further concentrate on the target topic.

Towards a systematic description of the field using bibliometric analysis: malware evolution

Malware is a blanket term for Trojan, viruses, spyware, worms, and other files that are purposely created to harm computers, mobile devices, or computer networks. Malware commonly steals, encrypts, damages, and causes a mess in these devices. The growth of malware attacks has a consequence on the growth and attractiveness of mobile features in mobile devices. Most malware research aims to probe the different methods of preventing, analysing, and detecting malware attacks. This paper aims to demonstrate an exhaustive knowledge map of the Android malware by collecting a ten (10) year dataset from the Web of Science database. A bibliometric analysis was employed for analysing articles published between 2010 and 2019. Using the keyword "malware", 5622 articles were retrieved. After scrutinising with the keywords of "Android malware", 1278 articles were then collected. This study provides an overview of the articles, productivity, research area, the Web of Science categories, authors, high-cited articles, institutions, and impact journals examining malware. Research activities are continued by placing terms in the classification of malware detection systems that outline important areas in malware research. From the analysis, it can be concluded that the highest number of publications focusing on malware studies came from the continent of Asia. Additionally, this study discusses the challenges of malware studies in the recent research studies as well as the future direction.