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Science mapping analysis of computed tomography-derived fractional flow reverse: a bibliometric review from 2012 to 2022.
Zhang, Xiaohan; Zhu, Xueping; Jiang, Yuchen; Wang, Huan; Guo, Zezhen; Du, Bai; Hu, Yuanhui.
Afiliação
  • Zhang X; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Zhu X; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Jiang Y; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Wang H; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Guo Z; Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
  • Du B; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Hu Y; Department of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Quant Imaging Med Surg ; 13(9): 5605-5621, 2023 Sep 01.
Article em En | MEDLINE | ID: mdl-37711816
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 multi-disciplinary 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Quant Imaging Med Surg Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Quant Imaging Med Surg Ano de publicação: 2023 Tipo de documento: Article