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Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization

  • 0Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou 313000, Zhejiang Province, China.
World Journal of Gastrointestinal Oncology +

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June 9, 2025

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June 9, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Deep learning (DL) is revolutionizing colorectal cancer (CRC) research, aiding in diagnosis and prognosis. This bibliometric analysis maps the field

Area Of Science

  • Bibliometrics
  • Artificial Intelligence
  • Oncology

Background

  • Colorectal cancer (CRC) poses a significant global health burden.
  • Deep learning (DL) shows promise in advancing CRC diagnosis, identification, localization, classification, and prognosis.
  • Limited bibliometric analyses exist for DL in CRC research.

Purpose Of The Study

  • To conduct a bibliometric analysis of DL in CRC research.
  • To visualize the current research landscape and trends.
  • To identify future research directions and hotspots.

Main Methods

  • Bibliometric analysis of publications from Web of Science (2011-2023).
  • Utilized Scimago Graphica, VOSviewer, and CiteSpace for data visualization and analysis.
  • Included nation, institution, journal, author, reference, and keyword indicators.

Main Results

  • 1275 publications were analyzed from 74 countries.
  • China, the United States, and Japan are leading contributors.
  • Key institutions, journals, authors, and highly cited references were identified, with 'colorectal cancer' as a prominent keyword cluster.

Conclusions

  • DL is a rapidly growing field with significant applications in CRC management.
  • The study maps the current state and active research directions in DL for CRC.
  • DL is expected to remain a central focus for future CRC research.

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