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Science mapping analysis of computed tomography-derived fractional flow reverse: a bibliometric review from 2012 to 2022
被引:1
|作者:
Zhang, Xiaohan
[1
]
Zhu, Xueping
[1
]
Jiang, Yuchen
[1
]
Wang, Huan
[1
]
Guo, Zezhen
[2
]
Du, Bai
[1
]
Hu, Yuanhui
[1
]
机构:
[1] China Acad Chinese Med Sci, Guanganmen Hosp, Dept Cardiovasc Dis, 5 Beixiange, Beijing 100053, Peoples R China
[2] Macquarie Univ, Fac Med Hlth & Human Sci, Sydney, NSW, Australia
关键词:
Bibliometrics;
computed tomography-derived fractional flow reserve (CT-FFR);
coronary heart disease (CHD);
CiteSpace;
artificial intelligence (AI);
TRANSLUMINAL ATTENUATION GRADIENT;
CORONARY-ARTERY-DISEASE;
STABLE CHEST-PAIN;
DIAGNOSTIC-ACCURACY;
CT ANGIOGRAPHY;
EMERGING TRENDS;
RESERVE;
OUTCOMES;
STENOSIS;
FFRCT;
D O I:
10.21037/qims-22-1094
中图分类号:
R8 [特种医学];
R445 [影像诊断学];
学科分类号:
1002 ;
100207 ;
1009 ;
摘要:
Background: Computed tomography-derived fractional flow reserve (CT-FFR) is a non-invasive imagological examination used for diagnosing suspected coronary atherosclerotic heart disease, providing the morphological and functional value on a three-dimensional (3D) coronary artery model. This article aimed to collate the existing knowledge and predict this novel technology's future research hotspots. Methods: To collect data, 1,712 articles were retrieved from the Web of Science Core Collection (WoSCC) database from 2012-2022. CiteSpace5.8.R3 was used to visually analyze the research status and predict future research hotspots. Results: Firstly, the United States, China, and the Netherlands were identified as the countries having published the most articles about CT-FFR. Jonathan Leipsic's group ranked first for the highest number of published articles. Secondly, the visualized analysis indicated that the exploration of CT-FFR is multidisciplinary and involves cardiology, radiology, engineering, and computer science. Thirdly, the hotspots in this field, which were inferred from the keyword distribution and clustering, included the following: "diagnostic performance", "accuracy", and the "prognostic value" of CT-FFR, and comparison of CT-FFR and other imaging methods sharing similarities. The research frontiers included technologies utilized to obtain more accurate CT-FFR values, such as artificial intelligence (AI) and deep learning. Conclusions: As the first visualized bibliometric analysis on CT-FFR, this study captured the current accumulated information in this field and offer more insight and guidance for future research.
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页码:5605 / 5621
页数:17
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