Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Meta-analysis in periprosthetic joint infection: a global bibliometric analysis

2020 ◽  
Author(s):  
Cheng Li ◽  
Cristina Ojeda Thies ◽  
Chi Xu ◽  
Andrej Trampuz
Keyword(s):  
Risk Factors ◽  
Bibliometric Analysis ◽  
Periprosthetic Joint Infection ◽  
Clinical Medicine ◽  
Meta Analysis ◽  
Joint Infection ◽  
Relevant Literature ◽  
The United States ◽  
Steroid Injections ◽  
Joint Replacement Surgery

Abstract Background: Periprosthetic joint infection (PJI) is the most serious complication of joint replacement surgery. Further comorbidities include bedsore, deep vein thrombosis, reinfection, or even death. An increasing number of researchers are focusing on this challenging complication. The aim of the present study was to estimate global PJI research based on bibliometrics from meta-analysis studies. Methods: A database search was performed in PubMed, Scopus, and Web of Science. Relevant studies were assessed using the bibliometric analysis. Results: A total of 117 articles were included. The most relevant literature on PJI was found on Scopus. China made the highest contributions to global research, followed by the United States and the United Kingdom. The institution with the most contributions from the University of Bristol. The journal with the highest number of publications was The Journal of Arthroplasty, whereas the Journal of Clinical Medicine had the shortest acceptance time. The day of the week with the greatest number of received and accepted manuscripts was Wednesday. Furthermore, the top three frequently used databases were Embase, MEDLINE, and Cochrane. The most frequent number of authors in meta-analysis studies was four. Most studies focused on the periprosthetic hip and knee. The alpha-defensin diagnostic test, preventive measures on antibiotics use, and risk factors of intra-articular steroid injections were the most popular topic in recent years. Conclusion: Based on the results of the present study, we found that there was no single database covered all relevant articles, the optimal method for bibliometric analysis is a combination of databases. The most popular research topics on PJI focused on alpha-defensin, antibiotic use, risk factors of intra-articular steroid injections, and the location of prosthetic hip and knee infection. Alpha-defensin appears to be a reliable tool for the diagnosis of PJI.

scholarly journals Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3532 ◽  
Author(s):  
Ryuji Hamamoto ◽  
Kruthi Suvarna ◽  
Masayoshi Yamada ◽  
Kazuma Kobayashi ◽  
Norio Shinkai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Medical Devices ◽  
The United States ◽  
Medical Data ◽  
Clinical Implementation ◽  
Medical Field ◽  
Core Technology ◽  
Artificial Intelligence Technology ◽  
History Of

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

scholarly journals The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

2019 ◽  
Vol 16 (12) ◽  
pp. 2150 ◽  
Author(s):  
Bach Xuan Tran ◽  
Roger S. McIntyre ◽  
Carl A. Latkin ◽  
Hai Thanh Phan ◽  
Giang Thu Vu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Bibliometric Analysis ◽  
Exploratory Factor Analysis ◽  
Depressive Disorders ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
The United States ◽  
Research Network ◽  
Dirichlet Allocation

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.

scholarly journals Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

2021 ◽  
Vol 83 ◽  
pp. 221-241
Author(s):  
Michele Avanzo ◽  
Massimiliano Porzio ◽  
Leda Lorenzon ◽  
Lisa Milan ◽  
Roberto Sghedoni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Medical Physics

scholarly journals Meta-analysis in periprosthetic joint infection: a global bibliometric analysis

2020 ◽  
Author(s):  
Cheng Li ◽  
Cristina Ojeda Thies ◽  
Chi Xu ◽  
Andrej Trampuz
Keyword(s):  
Risk Factors ◽  
Bibliometric Analysis ◽  
Periprosthetic Joint Infection ◽  
Clinical Medicine ◽  
Meta Analysis ◽  
Joint Infection ◽  
Relevant Literature ◽  
The United States ◽  
Steroid Injections ◽  
Joint Replacement Surgery

Abstract Background: Periprosthetic joint infection (PJI) is the most serious complication of joint replacement surgery. Further comorbidities include bedsore, deep vein thrombosis, reinfection, or even death. An increasing number of researchers are focusing on this challenging complication. The aim of the present study was to estimate global PJI research based on bibliometrics from meta-analysis studies. Methods: A database search was performed in PubMed, Scopus, and Web of Science. Relevant studies were assessed using the bibliometric analysis.Results: A total of 117 articles were included. The most relevant literature on PJI was found on Scopus. China made the highest contributions to global research, followed by the United States and the United Kingdom. The institution with the most contributions from the University of Bristol. The journal with the highest number of publications was The Journal of Arthroplasty, whereas the Journal of Clinical Medicine had the shortest acceptance time. Furthermore, the top three frequently used databases were Embase, MEDLINE, and Cochrane. The most frequent number of authors in meta-analysis studies was four. Most studies focused on the periprosthetic hip and knee. The alpha-defensin diagnostic test, preventive measures on antibiotics use, and risk factors of intra-articular steroid injections were the most popular topic in recent years. Conclusion: Based on the results of the present study, we found that there was no single database covered all relevant articles, the optimal method for bibliometric analysis is a combination of databases. The most popular research topics on PJI focused on alpha-defensin, antibiotic use, risk factors of intra-articular steroid injections, and the location of prosthetic hip and knee infection.

scholarly journals Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

2021 ◽  
Vol 10 (7) ◽  
pp. 1391
Author(s):  
Zeynettin Akkus ◽  
Yousof H. Aly ◽  
Itzhak Z. Attia ◽  
Francisco Lopez-Jimenez ◽  
Adelaide M. Arruda-Olson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Medicine ◽  
State Of The Art ◽  
Imaging Modality ◽  
Learning Ability ◽  
Imaging Features ◽  
Clinical Workflow ◽  
Imaging Tool ◽  
Image Quality Control ◽  
And Function

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

scholarly journals Application of Artificial Intelligence in Cardiovascular Imaging

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Panjiang Ma ◽  
Qiang Li ◽  
Jianbin Li
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cardiovascular Imaging ◽  
Development Trend ◽  
Imaging Data ◽  
Label Information

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure.

scholarly journals Osteopathic empirical research: a bibliometric analysis from 1966 to 2018

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chantal Morin ◽  
Isabelle Gaboury
Keyword(s):  
Bibliometric Analysis ◽  
Empirical Research ◽  
Scientific Literature ◽  
Case Reports ◽  
Empirical Studies ◽  
Body Part ◽  
The United States ◽  
Mesh Terms ◽  
Research Designs ◽  
System Body

Abstract Background Despite the increasing use of osteopathy, a manipulative complementary and alternative medicine therapy, in the general population, its efficacy continues to be debated. In this era of evidence-based practice, no studies have previously reviewed the scientific literature in the field to identify published knowledge, trends and gaps in empirical research. The aims of this bibliometric analysis are to describe characteristics of articles published on the efficacy of osteopathic interventions and to provide an overall portrait of their impacts in the scientific literature. Methods A bibliometric analysis approach was used. Articles were identified with searches using a combination of relevant MeSH terms and indexing keywords about osteopathy and research designs in MEDLINE and CINAHL databases. The following indicators were extracted: country of primary author, year of publication, journals, impact factor of the journal, number of citations, research design, participants’ age group, system/body part addressed, primary outcome, indexing keywords and types of techniques. Results A total of 389 articles met the inclusion criteria. The number of empirical studies doubled every 5 years, with the United States, Italy, Spain, and United Kingdom being the most productive countries. Twenty-three articles were cited over 100 times. Articles were published in 103 different indexed journals, but more than half (53.7%) of articles were published in one of three osteopathy-focused readership journals. Randomized control trials (n = 145; 37.3%) and case reports (n = 142; 36.5%) were the most common research designs. A total of 187 (48.1%) studies examined the effects of osteopathic interventions using a combination of techniques that belonged to two or all of the classic fields of osteopathic interventions (musculoskeletal, cranial, and visceral). Conclusion The number of osteopathy empirical studies increased significantly from 1980 to 2014. The productivity appears to be very much in sync with practice development and innovations; however, the articles were mainly published in osteopathic journals targeting a limited, disciplinary-focused readership.

scholarly journals SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

2021 ◽  
Vol 76 ◽  
pp. 6-14
Author(s):  
Narjes Benameur ◽  
Ramzi Mahmoudi ◽  
Soraya Zaid ◽  
Younes Arous ◽  
Badii Hmida ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Imaging Techniques
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