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Kidney medicine meets computer vision: a bibliometric analysis.
Junren Chen1,2,3, Rui Chen4, Liangyin Chen2
1Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
International Urology and Nephrology
|May 30, 2024
Summary
Computer vision (CV) is increasingly used in kidney medicine for diagnosis and treatment. Research shows a growing trend in publications, with AI and 3D imaging becoming more prominent in kidney disease research.
Area of Science:
- Nephrology
- Medical Imaging
- Computer Vision
Background:
- Computer vision (CV) offers significant potential for advancing kidney disease examination, diagnosis, and treatment.
- The application of CV in kidney medicine is a rapidly evolving field with growing research interest.
Purpose of the Study:
- To conduct a bibliometric analysis of research on computer vision applications in kidney medicine.
- To explore the evolving research landscape, trends, and key contributors in this interdisciplinary field.
Main Methods:
- Bibliometric analysis of publications from the Web of Science Core Collection (1900-2022).
- Analysis of publication trends, influential sources, prolific researchers, and institutions.
- Network visualization using Python, Matplotlib, Seaborn, HistCite, and Vosviewer.
Main Results:
- A notable increase in publications applying CV to kidney medicine.
- Dominant research themes include medical image processing, diagnosis, and surgical procedures.
- The United States leads in publications, with deep learning and machine learning as key techniques; research is shifting towards AI, 3D data, and diverse modalities.
Conclusions:
- Bibliometric analysis provides a scientometric overview of CV in kidney medicine.
- Identified emerging trends, key sources, leading institutions, and popular topics.
- Findings offer valuable insights for future research directions in CV for kidney medicine.
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