A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research

Chen, Xieling; Zhang, Xinxin; Xie, Haoran; Tao, Xiaohui; Wang, Fu Lee; Xie, Naiming; Hao, Tianyong

doi:10.1007/s11042-020-09062-7

Cited by 10 publications

(12 citation statements)

References 139 publications

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“…Although some studies have not yet been clinically translated, they may direct future research and provide potential biomarkers and therapeutic targets as the subject develops in the future. Bibliometric analysis is an effective method that is utilized in diverse areas of study [12][13][14][15][16][18][19][20]. A bibliometric analysis performed in 2019 [19] showed that 3 of the top 10 most cited articles in nephrology were on the subject of RAAS blockade usage in DKD.…”

Section: Discussionmentioning

confidence: 99%

“…Bibliometric analysis is a method utilized in many fields to illustrate the landscape of subjects [12,13]. Based on CT, bibliometric analysis objectively includes articles of subject on subject in the analysis and reflects the evolution of a subject, trends of popular topics and collaborative relationships among researchers and countries [14][15][16]. To determine the influential research, the distribution of disparate topics and the evolution of research trends, we performed a bibliometric analysis of the top 100 most cited articles in clinical and experimental studies of DKD.…”

Section: Introductionmentioning

confidence: 99%

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The evolution and future of diabetic kidney disease research: a bibliometric analysis

Wei

Jiang

2021

BMC Nephrol

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Background Diabetic kidney disease (DKD) is one of the most important complications of diabetic mellitus. It is essential for nephrologists to understand the evolution and development trends of DKD. Methods Based on the total cited numbers in the Web of Science Core Collection, which was searched through September 28th, 2020, we performed a bibliometric analysis of the top 100 most cited full-length original articles on the subject of DKD. The timespans, authors, contributions, subcategories, and topics of those 100 articles were analysed. In addition, the evolution of topics in DKD research was investigated. Results There were 23,968 items under the subject of DKD in the Web of Science Core Collection. The top 100 cited articles, published from 1999 to 2017, were cited 38,855 times in total. Researchers from the USA contributed the most publications. The number of articles included in ‘Experimental studies (EG)’, ‘Clinical studies (CS)’, ‘Epidemiological studies (ES)’, and ‘Pathological and pathophysiological studies (PP)’ were 65, 26, 7, and 2, respectively. Among the 15 topics, the most popular topic is the renin-angiotensin-aldosterone system (RAAS), occurring in 26 articles, including 6 of the top 10 most cited articles. The evolution of topics reveals that the role of RAAS inhibitor is a continuous hotspot, and sodium-glucose cotransporter 2 (SGLT-2) inhibitor and glucagon-like peptide 1 (GLP-1) agonist are two renoprotective agents which represent novel therapeutic methods in DKD. In addition, the 26 clinical studies among the top 100 most cited articles were highlighted, as they help guide clinical practice to better serve patients. Conclusions This bibliometric analysis of the top 100 most cited articles revealed important studies, popular topics, and trends in DKD research to assist researchers in further understanding the subject.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The evolution and future of diabetic kidney disease research: a bibliometric analysis

Wei

Jiang

2021

BMC Nephrol

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“…Yuan Wang et al [ 19 ] proposed a dual-input v-mesh fully convolutional network with the input of raw CT images and algorithmically processed images to improve the contrast between the pancreas and other soft tissues, and enhanced feature extraction by adding an attention module to improve the accuracy of pancreas segmentation. The above methods are able to handle the medical image segmentation problem in an improved way, being combined with FCN [ 20 ] or based on the U-Net network [ 21 ], and have achieved good segmentation results in respect of different medical images [ 22 ]. However, when dealing with the sequence relationship between cardiac MRI images and the multi-scale characteristics of the left atrial structure within the images, they struggle to simulate the continuous relationship between sequential image slices, and cannot capture the contextual information at different scales, thus failing to meet the demand for accurate segmentation.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning

Zuo

et al. 2023

Sensors

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Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network’s ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.

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“…Such analyses have been performed on multiple domains till date, such as a journal specific study of publication and citations structure [ 57 , 61 , 95 ]. In addition to these, there are studies which provided the results of a scientometric analysis performed to gain insight into research areas such as the study of aggregation operators [ 11 ], industry 4.0 revolution [ 58 ], brain MRI research [ 18 ], multimedia big data [ 43 ], etc. Similarly in the medical domain, the scientific outputs were tracked within the topics of the Ebola outbreak [ 93 ], global malaria vaccine [ 35 ], and Dengue research [ 96 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

Shukla

Seth

Muhuri

2023

Multimed Tools Appl

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With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

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A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research

Cited by 10 publications

References 139 publications

The evolution and future of diabetic kidney disease research: a bibliometric analysis

The evolution and future of diabetic kidney disease research: a bibliometric analysis

Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

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