Bibliometric and visualized analysis of ultrasound combined with microbubble therapy technology from 2009 to 2023
Bibliographic record
Abstract
Background In recent years, with the rapid advancement of fundamental ultrasonography research, the application of ultrasound in disease treatment has progressively increased. An increasing body of research indicates that microbubbles serve not only as contrast agents but also in conjunction with ultrasound, enhancing cavitation effects and facilitating targeted drug delivery, thereby augmenting therapeutic efficacy. The objective of this study is to explore the current status and prevailing research trends in this field from 2009 to 2023 through bibliometric analysis and to forecast future developmental trajectories. Methods We selected the Science Citation Index Expanded (SCI-Expanded) from the Web of Science Core Collection (WOSCC) as our primary data source. On 19 January 2024, we conducted a comprehensive search encompassing all articles and reviews published between 2009 and 2023 and utilized the bibliometric online analysis platform, CiteSpace and VOSviewer software to analyze countries/regions, institutions, authors, keywords, and references, used Microsoft Excel 2021 to visualize the trends of the number of articles published by year. Results Between 1 January 2009, and 31 December 2023, 3,326 publications on ultrasound combined with microbubble therapy technology were included. There were a total of 2,846 articles (85.6%) and 480 reviews (14.4%) from 13,062 scholars in 68 countries/regions published in 782 journals. China and the United States emerged as the primary contributors in this domain. In terms of publication output and global institutional collaboration, the University of Toronto in Canada has made the most significant contribution to this field. Professor Kullervo Hynynen has achieved remarkable accomplishments in this area. Ultrasound in Medicine and Biology is at the core of the publishing of research on ultrasound combined with microbubble therapy technology. Keywords such as “sonodynamic therapy,” “oxygen,” “loaded microbubbles” and “Alzheimer’s disease” indicate emerging trends in the field and hold the potential to evolve into significant areas of future investigation. Conclusion This study provides a summary of the key contributions of ultrasound combined with microbubble therapy to the field’s development over the past 15 years and delves into the historical underpinnings and contemporary trends of ultrasound combined with microbubble therapy technology, providing valuable guidance for researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.163 | 0.221 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".